2019 |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Kernel cross-view collaborative representation based classification for person re-identification Journal Article Journal of Visual Communication and Image Representation, 58 (1), pp. 304-315, 2019. Links | BibTeX | Tags: Kernel collaborative representation based classification, Person Re-Identification @article{Prates:2019:JVCI, title = {Kernel cross-view collaborative representation based classification for person re-identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, doi = {https://doi.org/10.1016/j.jvcir.2018.12.003}, year = {2019}, date = {2019-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {58}, number = {1}, pages = {304-315}, keywords = {Kernel collaborative representation based classification, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
2018 |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Kernel multiblock partial least squares for a scalable and multicamera person reidentification system Journal Article pp. 1-33, 2018. Links | BibTeX | Tags: Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @article{Prates:2018:JEI, title = {Kernel multiblock partial least squares for a scalable and multicamera person reidentification system}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/article.pdf}, year = {2018}, date = {2018-06-25}, booktitle = {Journal of Electronic Imaging}, pages = {1-33}, keywords = {Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
2016 |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Kernel Hierarchical PCA for Person Re-Identification Inproceedings IAPR International Conference on Pattern Recognition (ICPR), 2016. Abstract | Links | BibTeX | Tags: Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @inproceedings{Prates2016ICPR, title = {Kernel Hierarchical PCA for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Kernel_HPCA_final.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, abstract = {Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions.}, keywords = {Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions. |
de Prates, Raphael Felipe Carvalho; Dutra, Cristianne Rodrigues Santos; Schwartz, William Robson Predominant Color Name Indexing Structure for Person Re-Identification Inproceedings IEEE International Conference on Image Processing (ICIP), 2016. Abstract | Links | BibTeX | Tags: GigaFrames, Person Re-Identification, VER+ @inproceedings{Prates2016ICIP, title = {Predominant Color Name Indexing Structure for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Cristianne Rodrigues Santos Dutra and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ICIP_Prates.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, abstract = {The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.}, keywords = {GigaFrames, Person Re-Identification, VER+}, pubstate = {published}, tppubtype = {inproceedings} } The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery. |
de Prates, Raphael Felipe Carvalho; Oliveira, Marina Santos; Schwartz, William Robson Kernel Partial Least Squares for Person Re-Identification Inproceedings IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. Abstract | Links | BibTeX | Tags: DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification @inproceedings{Prates2016AVSS, title = {Kernel Partial Least Squares for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Marina Santos Oliveira and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/egpaper_for_DoubleBlindReview.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)}, abstract = {Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S.}, keywords = {DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S. |
Dutra, Cristianne Rodrigues Santos Técnicas Otimizadas para Reidentificaçâo de Pessoas Masters Thesis Federal University of Minas Gerais, 2016. Links | BibTeX | Tags: DeepEyes, GigaFrames, Person Re-Identification, VER+ @mastersthesis{Dutra:2016:MSc, title = {Técnicas Otimizadas para Reidentificaçâo de Pessoas}, author = {Cristianne Rodrigues Santos Dutra}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/thesis_2016_Cristianne.pdf}, year = {2016}, date = {2016-01-01}, school = {Federal University of Minas Gerais}, keywords = {DeepEyes, GigaFrames, Person Re-Identification, VER+}, pubstate = {published}, tppubtype = {mastersthesis} } |
2015 |
Prado, G L; Schwartz, William Robson; Pedrini, Helio A Verify-Correct Approach to Person Re-identification Based on Partial Least Squares Signatures Inproceedings International Conference on Biometrics, pp. 1-7, 2015. Links | BibTeX | Tags: Partial Least Squares, Person Re-Identification, SmartView, VER+ @inproceedings{Prado:2015:ICB, title = {A Verify-Correct Approach to Person Re-identification Based on Partial Least Squares Signatures}, author = {G L Prado and William Robson Schwartz and Helio Pedrini}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICB_Prado.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {International Conference on Biometrics}, pages = {1-7}, series = {Lecture Notes in Computer Science}, keywords = {Partial Least Squares, Person Re-Identification, SmartView, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson CBRA: Color-Based Ranking Aggregation for Person Re-Identification Inproceedings IEEE International Conference on Image Processing (ICIP), pp. 1-5, 2015. Links | BibTeX | Tags: CBRA, GigaFrames, Person Re-Identification, Ranking Aggregation, SmartView, VER+ @inproceedings{Prates:2015:ICB, title = {CBRA: Color-Based Ranking Aggregation for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICIP_Prates.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, pages = {1-5}, keywords = {CBRA, GigaFrames, Person Re-Identification, Ranking Aggregation, SmartView, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
de Prates, Raphael Felipe Carvalho; Schwartz, William Robson Appearance-Based Person Re-identification by Intra-Camera Discriminative Models and Rank Aggregation Inproceedings International Conference on Biometrics, pp. 1-8, 2015. Links | BibTeX | Tags: Person Re-Identification, SmartView @inproceedings{Prates:2015:ICBb, title = {Appearance-Based Person Re-identification by Intra-Camera Discriminative Models and Rank Aggregation}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICB_Prates.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {International Conference on Biometrics}, pages = {1-8}, series = {Lecture Notes in Computer Science}, keywords = {Person Re-Identification, SmartView}, pubstate = {published}, tppubtype = {inproceedings} } |
2014 |
Dutra, Cristianne Rodrigues Santos; Rocha, M C; Schwartz, William Robson Person Re-Identification Based on Weighted Indexing Structures Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 359-366, Springer International Publishing, 2014. Links | BibTeX | Tags: ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, SmartView @inproceedings{Dutra:2014:CIARP, title = {Person Re-Identification Based on Weighted Indexing Structures}, author = {Cristianne Rodrigues Santos Dutra and M C Rocha and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2014_CIARP_Dutra.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, volume = {8827}, pages = {359-366}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, keywords = {ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, SmartView}, pubstate = {published}, tppubtype = {inproceedings} } |
2013 |
Prado, Gabriel Lorencetti; Schwartz, William Robson; Pedrini, Helio Person Re-identification Using Partial Least Squares Appearance Modeling Incollection Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 382–390, 2013. Links | BibTeX | Tags: ARDOP, Person Re-Identification @incollection{prado2013person, title = {Person Re-identification Using Partial Least Squares Appearance Modeling}, author = {Gabriel Lorencetti Prado and William Robson Schwartz and Helio Pedrini}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2013-Person-Re-identification-Using-Partial-Least-Squares-Appearance-Modeling.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications}, pages = {382--390}, keywords = {ARDOP, Person Re-Identification}, pubstate = {published}, tppubtype = {incollection} } |
Dutra, Cristianne Rodrigues Santos; Souza, T; Alves, R; Schwartz, William Robson; Oliveira, L R Re-identifying People based on Indexing Structure and Manifold Appearance Modeling Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 218-225, 2013. Links | BibTeX | Tags: ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, Riemannian Manifold @inproceedings{Dutra:2013:SIBGRAPIb, title = {Re-identifying People based on Indexing Structure and Manifold Appearance Modeling}, author = {Cristianne Rodrigues Santos Dutra and T Souza and R Alves and William Robson Schwartz and L R Oliveira}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/paper_2013_SIBGRAPI.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {218-225}, keywords = {ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, Riemannian Manifold}, pubstate = {published}, tppubtype = {inproceedings} } |
2012 |
Schwartz, William Robson Scalable People Re-Identification Based on a One-Against-Some Classification Scheme Inproceedings IEEE International Conference on Image Processing, 2012. Links | BibTeX | Tags: One-Against-All Classification Scheme, Person Re-Identification @inproceedings{Schwartz:2012:ICIP, title = {Scalable People Re-Identification Based on a One-Against-Some Classification Scheme}, author = {William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-Scalable-People-Re-Identification-Based-on-a-One-Against-Some-Classification-Scheme.pdf}, year = {2012}, date = {2012-01-01}, booktitle = {IEEE International Conference on Image Processing}, keywords = {One-Against-All Classification Scheme, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } |
2009 |
Schwartz, William Robson; Davis, L S Learning Discriminative Appearance-Based Models Using Partial Least Squares Inproceedings Brazilian Symposium on Computer Graphics and Image Processing, 2009. Links | BibTeX | Tags: ETHZ, One-Against-All Classification Scheme, Partial Least Squares, Person Re-Identification @inproceedings{Schwartz:2009:SIBGRAPI, title = {Learning Discriminative Appearance-Based Models Using Partial Least Squares}, author = {William Robson Schwartz and L S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2009-Learning-Discriminative-Appearance-Based-Models-Using-Partial-Least-Squares.pdf}, year = {2009}, date = {2009-01-01}, booktitle = {Brazilian Symposium on Computer Graphics and Image Processing}, keywords = {ETHZ, One-Against-All Classification Scheme, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } |
2019 |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Kernel cross-view collaborative representation based classification for person re-identification Journal Article Journal of Visual Communication and Image Representation, 58 (1), pp. 304-315, 2019. Links | BibTeX | Tags: Kernel collaborative representation based classification, Person Re-Identification @article{Prates:2019:JVCI, title = {Kernel cross-view collaborative representation based classification for person re-identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, doi = {https://doi.org/10.1016/j.jvcir.2018.12.003}, year = {2019}, date = {2019-01-01}, journal = {Journal of Visual Communication and Image Representation}, volume = {58}, number = {1}, pages = {304-315}, keywords = {Kernel collaborative representation based classification, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
2018 |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Kernel multiblock partial least squares for a scalable and multicamera person reidentification system Journal Article pp. 1-33, 2018. Links | BibTeX | Tags: Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @article{Prates:2018:JEI, title = {Kernel multiblock partial least squares for a scalable and multicamera person reidentification system}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/article.pdf}, year = {2018}, date = {2018-06-25}, booktitle = {Journal of Electronic Imaging}, pages = {1-33}, keywords = {Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {article} } |
2016 |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Kernel Hierarchical PCA for Person Re-Identification Inproceedings IAPR International Conference on Pattern Recognition (ICPR), 2016. Abstract | Links | BibTeX | Tags: Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification @inproceedings{Prates2016ICPR, title = {Kernel Hierarchical PCA for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Kernel_HPCA_final.pdf}, year = {2016}, date = {2016-12-13}, booktitle = {IAPR International Conference on Pattern Recognition (ICPR)}, abstract = {Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions.}, keywords = {Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person ReID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions. |
Raphael Felipe Carvalho de Prates; Cristianne Rodrigues Santos Dutra; William Robson Schwartz Predominant Color Name Indexing Structure for Person Re-Identification Inproceedings IEEE International Conference on Image Processing (ICIP), 2016. Abstract | Links | BibTeX | Tags: GigaFrames, Person Re-Identification, VER+ @inproceedings{Prates2016ICIP, title = {Predominant Color Name Indexing Structure for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Cristianne Rodrigues Santos Dutra and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2016_ICIP_Prates.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, abstract = {The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.}, keywords = {GigaFrames, Person Re-Identification, VER+}, pubstate = {published}, tppubtype = {inproceedings} } The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from partbased models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery. |
Raphael Felipe Carvalho de Prates; Marina Santos Oliveira; William Robson Schwartz Kernel Partial Least Squares for Person Re-Identification Inproceedings IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016. Abstract | Links | BibTeX | Tags: DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification @inproceedings{Prates2016AVSS, title = {Kernel Partial Least Squares for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and Marina Santos Oliveira and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/egpaper_for_DoubleBlindReview.pdf}, year = {2016}, date = {2016-09-25}, booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)}, abstract = {Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S.}, keywords = {DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S. |
Cristianne Rodrigues Santos Dutra Técnicas Otimizadas para Reidentificaçâo de Pessoas Masters Thesis Federal University of Minas Gerais, 2016. Links | BibTeX | Tags: DeepEyes, GigaFrames, Person Re-Identification, VER+ @mastersthesis{Dutra:2016:MSc, title = {Técnicas Otimizadas para Reidentificaçâo de Pessoas}, author = {Cristianne Rodrigues Santos Dutra}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/thesis_2016_Cristianne.pdf}, year = {2016}, date = {2016-01-01}, school = {Federal University of Minas Gerais}, keywords = {DeepEyes, GigaFrames, Person Re-Identification, VER+}, pubstate = {published}, tppubtype = {mastersthesis} } |
2015 |
G L Prado; William Robson Schwartz; Helio Pedrini A Verify-Correct Approach to Person Re-identification Based on Partial Least Squares Signatures Inproceedings International Conference on Biometrics, pp. 1-7, 2015. Links | BibTeX | Tags: Partial Least Squares, Person Re-Identification, SmartView, VER+ @inproceedings{Prado:2015:ICB, title = {A Verify-Correct Approach to Person Re-identification Based on Partial Least Squares Signatures}, author = {G L Prado and William Robson Schwartz and Helio Pedrini}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICB_Prado.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {International Conference on Biometrics}, pages = {1-7}, series = {Lecture Notes in Computer Science}, keywords = {Partial Least Squares, Person Re-Identification, SmartView, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Raphael Felipe Carvalho de Prates; William Robson Schwartz CBRA: Color-Based Ranking Aggregation for Person Re-Identification Inproceedings IEEE International Conference on Image Processing (ICIP), pp. 1-5, 2015. Links | BibTeX | Tags: CBRA, GigaFrames, Person Re-Identification, Ranking Aggregation, SmartView, VER+ @inproceedings{Prates:2015:ICB, title = {CBRA: Color-Based Ranking Aggregation for Person Re-Identification}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICIP_Prates.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, pages = {1-5}, keywords = {CBRA, GigaFrames, Person Re-Identification, Ranking Aggregation, SmartView, VER+}, pubstate = {published}, tppubtype = {inproceedings} } |
Raphael Felipe Carvalho de Prates; William Robson Schwartz Appearance-Based Person Re-identification by Intra-Camera Discriminative Models and Rank Aggregation Inproceedings International Conference on Biometrics, pp. 1-8, 2015. Links | BibTeX | Tags: Person Re-Identification, SmartView @inproceedings{Prates:2015:ICBb, title = {Appearance-Based Person Re-identification by Intra-Camera Discriminative Models and Rank Aggregation}, author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2015_ICB_Prates.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {International Conference on Biometrics}, pages = {1-8}, series = {Lecture Notes in Computer Science}, keywords = {Person Re-Identification, SmartView}, pubstate = {published}, tppubtype = {inproceedings} } |
2014 |
Cristianne Rodrigues Santos Dutra; M C Rocha; William Robson Schwartz Person Re-Identification Based on Weighted Indexing Structures Inproceedings Iberoamerican Congress on Pattern Recognition (CIARP), pp. 359-366, Springer International Publishing, 2014. Links | BibTeX | Tags: ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, SmartView @inproceedings{Dutra:2014:CIARP, title = {Person Re-Identification Based on Weighted Indexing Structures}, author = {Cristianne Rodrigues Santos Dutra and M C Rocha and William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2014_CIARP_Dutra.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {Iberoamerican Congress on Pattern Recognition (CIARP)}, volume = {8827}, pages = {359-366}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, keywords = {ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, SmartView}, pubstate = {published}, tppubtype = {inproceedings} } |
2013 |
Gabriel Lorencetti Prado; William Robson Schwartz; Helio Pedrini Person Re-identification Using Partial Least Squares Appearance Modeling Incollection Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 382–390, 2013. Links | BibTeX | Tags: ARDOP, Person Re-Identification @incollection{prado2013person, title = {Person Re-identification Using Partial Least Squares Appearance Modeling}, author = {Gabriel Lorencetti Prado and William Robson Schwartz and Helio Pedrini}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2013-Person-Re-identification-Using-Partial-Least-Squares-Appearance-Modeling.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications}, pages = {382--390}, keywords = {ARDOP, Person Re-Identification}, pubstate = {published}, tppubtype = {incollection} } |
Cristianne Rodrigues Santos Dutra; T Souza; R Alves; William Robson Schwartz; L R Oliveira Re-identifying People based on Indexing Structure and Manifold Appearance Modeling Inproceedings Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 218-225, 2013. Links | BibTeX | Tags: ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, Riemannian Manifold @inproceedings{Dutra:2013:SIBGRAPIb, title = {Re-identifying People based on Indexing Structure and Manifold Appearance Modeling}, author = {Cristianne Rodrigues Santos Dutra and T Souza and R Alves and William Robson Schwartz and L R Oliveira}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/03/paper_2013_SIBGRAPI.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)}, pages = {218-225}, keywords = {ARDOP, Indexing Structure, Inverted Lists, Person Re-Identification, Riemannian Manifold}, pubstate = {published}, tppubtype = {inproceedings} } |
2012 |
William Robson Schwartz Scalable People Re-Identification Based on a One-Against-Some Classification Scheme Inproceedings IEEE International Conference on Image Processing, 2012. Links | BibTeX | Tags: One-Against-All Classification Scheme, Person Re-Identification @inproceedings{Schwartz:2012:ICIP, title = {Scalable People Re-Identification Based on a One-Against-Some Classification Scheme}, author = {William Robson Schwartz}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2012-Scalable-People-Re-Identification-Based-on-a-One-Against-Some-Classification-Scheme.pdf}, year = {2012}, date = {2012-01-01}, booktitle = {IEEE International Conference on Image Processing}, keywords = {One-Against-All Classification Scheme, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } |
2009 |
William Robson Schwartz; L S Davis Learning Discriminative Appearance-Based Models Using Partial Least Squares Inproceedings Brazilian Symposium on Computer Graphics and Image Processing, 2009. Links | BibTeX | Tags: ETHZ, One-Against-All Classification Scheme, Partial Least Squares, Person Re-Identification @inproceedings{Schwartz:2009:SIBGRAPI, title = {Learning Discriminative Appearance-Based Models Using Partial Least Squares}, author = {William Robson Schwartz and L S Davis}, url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/2009-Learning-Discriminative-Appearance-Based-Models-Using-Partial-Least-Squares.pdf}, year = {2009}, date = {2009-01-01}, booktitle = {Brazilian Symposium on Computer Graphics and Image Processing}, keywords = {ETHZ, One-Against-All Classification Scheme, Partial Least Squares, Person Re-Identification}, pubstate = {published}, tppubtype = {inproceedings} } |