Carlo Colombo
Alberto Del Bimbo
ABSTRACT
This paper addresses the problem of classifying actions performed by a human subject in a video sequence. A representation eigenspace approach based on the visual appearance is used to train the classifier. Before dimensionality reduction exploiting the PCA/LLE algorithms, a high dimensional description of each frame of the video sequence is constructed, based on foreground blob analysis. The classification task is performed by matching incrementally the reduced representation of the test image sequence against each of the learned ones, and accumulating matching scores until a decision is obtained; to this aim, two different metrics are introduced and evaluated. Experimental results demonstrate that the approach is accurate enough and feasible for behavior classification. Furthermore, we argue that the choice of both the feature descriptor and the metric for the matching score can dramatically influence the performance of the results.
- J. Aggarwal and Q. Cai. Human motion analysis: A review. CVIU, 73(3):428--440, 1999. Google Scholar
Digital Library
- M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. In Proc. of ICCV, pages 1395--1402, October 2005. Google ScholarDigital Library
- A. Bobick and J. Davis. The representation and recognition of action using temporal templates. PAMI, 23(3):257--267, 2001. Google ScholarDigital Library
- A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing action at a distance. In Proc. of ICCV, pages 726--733, October 2003. Google ScholarDigital Library
- A. Elgammal. Nonlinear generative models for dynamic shape and dynamic appearance. In Proc. of 2nd International Workshop on Generative-Model based vision, pages 182--189, 2004. Google ScholarDigital Library
- I. T. Jolliffe. Principal Component Analysis. Springer, 1986.Google ScholarCross Ref
- P. KaewTraKulPong and R. Bowden. An improved adaptive background mixture model for real-time tracking with shadow detection. In 2nd European Workshop on AVSS., 2001.Google Scholar
- I. Laptev and T. Lindeberg. Space time interest points. In Proc. of ICCV, pages 432--439, October 2003. Google ScholarDigital Library
- L. Z. Manor and M. Irani. Event-based analysis of video. In Proc. of CVPR, volume 2, pages 123--130, 2001.Google Scholar
- T. Moeslund and E. Granum. A survey of computer vision based human motion capture. CVIU, 81(3):231--268, 2001. Google ScholarDigital Library
- H. Murase and S. K. Nayar. Visual learning and recognition of 3d objects from appereance. IJCV, 14(5):39--50, 1995. Google ScholarDigital Library
- H. Murase and R. Sakai. Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters, 17(2):155--162, 1996. Google ScholarDigital Library
- T. Poggio and F. Girosi. Networks for approximation and learning. Proc. of the IEEE, 78(9), 1990.Google ScholarCross Ref
- R. Polana and R. Nelson. Recognizing activities. In Proc. of CVPR, pages 815--818, 1994.Google ScholarCross Ref
- M. Rahaman and S. Ishikawa. Human motion recognition using an eigenspace. Pattern Recognition Letters, 26(6):687--697, 2005. Google ScholarDigital Library
- S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323--2326, 2000.Google ScholarCross Ref
- M. Turk and A. Pentland. Face recognition using eigenfaces. In Proc. of CVPR, pages 586--591, 1991.Google ScholarCross Ref
- L. Wang, W. Hu, and T. Tan. Recent developments in human motion analysis. Pattern Recognition, 36(3):585--601, 2003.Google Scholar
Index Terms
- Behavior monitoring through automatic analysis of video sequences
Recommendations
Mixture of Gaussians-Based Background Subtraction for Bayer-Pattern Image Sequences
This letter proposes a background subtraction method for Bayer-pattern image sequences. The proposed method models the background in a Bayer-pattern domain using a mixture of Gaussians (MoG) and classifies the foreground in an interpolated red, green, ...
Adaptive Synopsis of Non-Human Primates' Surveillance Video Based on Behavior Classification
MMM 2016: Proceedings, Part I, of the 22nd International Conference on MultiMedia Modeling - Volume 9516Non-human primates NHPs play a critical role in biomedical research. Automated monitoring and analysis of NHP's behaviors through the surveillance video can greatly support the NHP-related studies. However, little research work has been undertaken yet. ...
Automatic spectral video matting
This paper proposes automatic spectral video matting based on adaptive component detection and component-matching-based spectral matting. In the proposed automatic spectral video matting, adaptive component detection is used to automatically generate ...
Comments