Júnior, Carlos Antônio Caetano Motion-Based Representations for Activity Recognition Tese PhD Universidade Federal of Minas Gerais, 2020. @phdthesis{CarlosCaetano:2020:PhD,
title = {Motion-Based Representations for Activity Recognition},
author = {Carlos Antônio Caetano Júnior},
year = {2020},
date = {2020-01-27},
school = {Universidade Federal of Minas Gerais},
abstract = {In this dissertation we propose four different representations based on motion information for activity recognition. The first is a spatiotemporal local feature descriptor that extracts a robust set of statistical measures to describe motion patterns. This descriptor measures meaningful properties of co-occurrence matrices and captures local space-time characteristics of the motion through the neighboring optical flow magnitude and orientation. The second, is the proposal of a compact novel mid-level representation based on co-occurrence matrices of codewords. This representation expresses the distribution of the features at a given offset over feature codewords from a pre-computed codebook and encodes global structures in various local region-based features. The third representation, is the proposal of a novel temporal stream for two-stream convolutional networks that employs images computed from the optical flow magnitude and orientation to learn the motion in a better and richer manner. The method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Finally, the forth is a novel skeleton image representation to be used as input of convolutional neural networks (CNNs). The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Moreover, the representation has the advantage of combining the use of reference joints and a tree structure skeleton, incorporating different spatial relationships between the joints and preserving important spatial relations. The experimental evaluations carried out on challenging well-known activity recognition datasets (KTH, UCF Sports, HMDB51, UCF101, NTU RGB+D 60 and NTU RGB+D 120) demonstrated that the proposed representations achieved better or similar accuracy results in comparison to the state of the art, indicating the suitability of our approaches as video representations.},
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In this dissertation we propose four different representations based on motion information for activity recognition. The first is a spatiotemporal local feature descriptor that extracts a robust set of statistical measures to describe motion patterns. This descriptor measures meaningful properties of co-occurrence matrices and captures local space-time characteristics of the motion through the neighboring optical flow magnitude and orientation. The second, is the proposal of a compact novel mid-level representation based on co-occurrence matrices of codewords. This representation expresses the distribution of the features at a given offset over feature codewords from a pre-computed codebook and encodes global structures in various local region-based features. The third representation, is the proposal of a novel temporal stream for two-stream convolutional networks that employs images computed from the optical flow magnitude and orientation to learn the motion in a better and richer manner. The method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Finally, the forth is a novel skeleton image representation to be used as input of convolutional neural networks (CNNs). The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Moreover, the representation has the advantage of combining the use of reference joints and a tree structure skeleton, incorporating different spatial relationships between the joints and preserving important spatial relations. The experimental evaluations carried out on challenging well-known activity recognition datasets (KTH, UCF Sports, HMDB51, UCF101, NTU RGB+D 60 and NTU RGB+D 120) demonstrated that the proposed representations achieved better or similar accuracy results in comparison to the state of the art, indicating the suitability of our approaches as video representations. |
Caetano, Carlos; de Melo, Victor H C; Brémond, François; dos Santos, Jefersson A; Schwartz, William Robson Magnitude-Orientation Stream network and depth information applied to activity recognition Journal Article Em: Journal of Visual Communication and Image Representation, 63 , pp. 102596, 2019, ISSN: 1047-3203. @article{CAETANO2019102596,
title = {Magnitude-Orientation Stream network and depth information applied to activity recognition},
author = {Carlos Caetano and Victor H C de Melo and François Brémond and Jefersson A dos Santos and William Robson Schwartz},
url = {http://www.sciencedirect.com/science/article/pii/S1047320319302172},
doi = {https://doi.org/10.1016/j.jvcir.2019.102596},
issn = {1047-3203},
year = {2019},
date = {2019-01-01},
journal = {Journal of Visual Communication and Image Representation},
volume = {63},
pages = {102596},
abstract = {The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Moreover, we also employ depth information to use as a weighting scheme on the magnitude information to compensate the distance of the subjects performing the activity to the camera. Experimental results, carried on two well-known datasets (UCF101 and NTU), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Moreover, we also employ depth information to use as a weighting scheme on the magnitude information to compensate the distance of the subjects performing the activity to the camera. Experimental results, carried on two well-known datasets (UCF101 and NTU), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation. |
Caetano, Carlos; Bremond, Francois; Schwartz, William Robson Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference
Joints Inproceedings Em: Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2019. @inproceedings{Caetano:2019:SIBGRAPIb,
title = {Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference
Joints},
author = {Carlos Caetano and Francois Bremond and William Robson Schwartz},
url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_SIBGRAPI_Caetano.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)},
pages = {1-8},
keywords = {},
pubstate = {published},
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|
Caetano, Carlos; Souza, Jessica; Bremond, Francois; Santos, Jefersson; Schwartz, William Robson SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition Inproceedings Em: 16th International Conference on Advanced Video and Signal-based Surveillance (AVSS), pp. 1-6, 2019. @inproceedings{Caetano:2019:AVSSb,
title = {SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition},
author = {Carlos Caetano and Jessica Souza and Francois Bremond and Jefersson Santos and William Robson Schwartz},
url = {http://www.dcc.ufmg.br/~william/papers/paper_2019_AVSS_Caetano.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {16th International Conference on Advanced Video and Signal-based Surveillance (AVSS)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
de Melo, Victor Hugo Cunha; Santos, Jesimon Barreto; Junior, Carlos Antonio Caetano; Sena, Jessica; Penatti, Otavio A B; Schwartz, William Robson Object-based Temporal Segment Relational Network for Activity Recognition Inproceedings Em: Conference on Graphic, Patterns and Images (SIBGRAPI), pp. 1-8, 2018. @inproceedings{DeMelo:2018:SIBGRAPI,
title = {Object-based Temporal Segment Relational Network for Activity Recognition},
author = {Victor Hugo Cunha de Melo and Jesimon Barreto Santos and Carlos Antonio Caetano Junior and Jessica Sena and Otavio A B Penatti and William Robson Schwartz},
year = {2018},
date = {2018-09-21},
booktitle = {Conference on Graphic, Patterns and Images (SIBGRAPI)},
pages = {1-8},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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|
Junior, Carlos Antonio Caetano; dos Santos, Jefersson A; Schwartz, William Robson Statistical Measures from Co-occurrence of Codewords for Action Recognition Inproceedings Em: VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018. @inproceedings{Caetano:2018:VISAPP,
title = {Statistical Measures from Co-occurrence of Codewords for Action Recognition},
author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/VISAPP_2018_CarlosCaetano.pdf},
year = {2018},
date = {2018-01-27},
booktitle = {VISAPP 2018 - International Conference on Computer Vision Theory and Applications},
pages = {1-8},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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|
Colque, Rensso Victor Hugo Mora; Junior, Carlos Antonio Caetano; de Melo, Victor Hugo Cunha; Chavez, Guillermo Camara; Schwartz, William Robson Novel Anomalous Event Detection based on Human-object Interactions Inproceedings Em: VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018. @inproceedings{Colque:2018:VISAPP,
title = {Novel Anomalous Event Detection based on Human-object Interactions},
author = {Rensso Victor Hugo Mora Colque and Carlos Antonio Caetano Junior and Victor Hugo Cunha de Melo and Guillermo Camara Chavez and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/VISAPP_2018_92.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {VISAPP 2018 - International Conference on Computer Vision Theory and Applications},
pages = {1-8},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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|
Colque, Rensso Victor Hugo Mora; Junior, Carlos Antonio Caetano; de Andrade, Matheus Toledo Lustosa; Schwartz, William Robson Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos Journal Article Em: IEEE Transactions on Circuits and Systems for Video Technology, 27 (3), pp. 673-682, 2017. @article{Colque:2016:TCSVT,
title = {Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos},
author = {Rensso Victor Hugo Mora Colque and Carlos Antonio Caetano Junior and Matheus Toledo Lustosa de Andrade and William Robson Schwartz},
url = {http://dx.doi.org/10.1109/TCSVT.2016.2637778},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
volume = {27},
number = {3},
pages = {673-682},
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pubstate = {published},
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Junior, Carlos Antonio Caetano; de Melo, Victor Hugo Cunha; dos Santos, Jefersson Alex; Schwartz, William Robson Activity Recognition based on a Magnitude-Orientation Stream Network Inproceedings Em: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2017. @inproceedings{Caetano:2017:SIBGRAPI,
title = {Activity Recognition based on a Magnitude-Orientation Stream Network},
author = {Carlos Antonio Caetano Junior and Victor Hugo Cunha de Melo and Jefersson Alex dos Santos and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/paper_2017_SIBGRAPI_Caetano.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)},
pages = {1-8},
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Junior, Carlos Antonio Caetano; dos Santos, Jefersson A; Schwartz, William Robson Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor Inproceedings Em: IAPR International Conference on Pattern Recognition (ICPR), pp. 1-6, 2016. @inproceedings{Caetano:2016:ICPR,
title = {Optical Flow Co-occurrence Matrices: A Novel Spatiotemporal Feature Descriptor},
author = {Carlos Antonio Caetano Junior and Jefersson A dos Santos and William Robson Schwartz},
url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/ICPR2016.pdf},
year = {2016},
date = {2016-12-13},
booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},
pages = {1-6},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
