Person re-identification deals with the automatic tracking of individuals across cameras without overlapping field of view. Assisting security personnel in monitoring pedestrian behavior at wider areas with reduced costs is an important task. In person re-identification, we have a gallery set and the objective is to match a probe image with one of these individuals. Using image pairs, present in the training set, we can learn similarity models. One the main challenges in person re-identification is the higher inter-class similarities as opposed to intra-class similarities caused by similar dressing patterns and acquisition conditions (illumination, viewpoint, etc.). We have published works based on the combination of different color descriptors through a ranking aggregation strategy (CBRA) and the use of prototypes to indirectly handle with the camera transition problem.
Click here for a comprehensive list with results for several person re-identification datasets.
Matching People Across Surveillance Cameras PhD Thesis
Universidade Federal de Minas Gerais, 2019.
In: Journal of Visual Communication and Image Representation, 58 (1), pp. 304-315, 2019.
In: pp. 1-33, 2018.
Kernel Hierarchical PCA for Person Re-Identification Inproceedings
In: IAPR International Conference on Pattern Recognition (ICPR), 2016.
In: IEEE International Conference on Image Processing (ICIP), 2016.
In: IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2016.
Técnicas Otimizadas para Reidentificaçâo de Pessoas Masters Thesis
Federal University of Minas Gerais, 2016.
In: International Conference on Biometrics, pp. 1-7, 2015.
In: IEEE International Conference on Image Processing (ICIP), pp. 1-5, 2015.
In: International Conference on Biometrics, pp. 1-8, 2015.
In: Iberoamerican Congress on Pattern Recognition (CIARP), pp. 359-366, Springer International Publishing, 2014.
In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 382–390, 2013.
In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 218-225, 2013.
In: IEEE International Conference on Image Processing, 2012.
In: Brazilian Symposium on Computer Graphics and Image Processing, 2009.