Abstract
In this article, a new approach is presented for action recognition with only one non-calibrated camera. Invariance to view point is obtained with several acquisitions of the same action. The originality of the presented approach consists of characterizing sequences by a temporal succession of semi-global features, which are extracted from “space-time micro-volumes”. The advantages of the proposed approach is the use of robust features (estimated on several frames) associated to the ability to manage actions with variable duration and to easily segment the sequences with algorithms that are specific to time varying data. For the recognition, each view of each action is modeled by an Hidden Markov Model system. Results presented on 1614 sequences of everyday life actions like “walking”, “sitting down”, “bending down”, performed by several persons validate the proposed approach.
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References
Bigorgne, E., Achard, C., Devars, J.: Local Zernike Moments Vector for Content based Queries in Image Database. In: Machine Vision and Applications, Tokyo, Japan, pp. 327–330 (2000)
Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 257–267 (2001)
Chomat, O., Crowley, J.L.: Probabilistic recognition of activity using local appearance. In: IEEE International Conference on Computer Vision and Pattern Recognition, Colorado, USA (1999)
Cupillard, F., Avanzi, A., Brémond, F., Thonnat, M.: Video Understanding for Metro Surveillance. In: IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan (2004)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features. In: IEEE International workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), Beijing, China (2005)
Gavrila, D.M.: The visual analysis of human movement: a survey. Computer Vision and Image Understanding 73, 82–98 (1999)
Hongeng, S., Bremond, F., Nevatia, R.: Bayesian framework for video surveillance application. In: International Conference on Computer Vision, Barcelona, Spain (2000)
Hu, W., Tan, T., Wang, L., Maybank, S.: A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transaction on System, Man and Cybernetics 34, 334–352 (2004)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient Visual Event Detection using Volumetric Features. In: IEEE International Conference on Computer Vision, Beijing, China (2005)
Martin, J., Crowley, J.L.: An appearance based approach to gesture recognition. In: International Conference on Image Analysis and Processing, Florence, Italy (1997)
Mostafaoui, G., Achard, C., Milgram, M.: Real time tracking of multiple persons on color image sequences. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, Springer, Heidelberg (2005)
Pierobon, M., Marcon, M., Sarti, A., Tubaro, S.: Clustering of human actions using invariant body shape descriptor and dynamic time warping. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), Como, Italy, IEEE, Los Alamitos (2005)
Porikli, F., Tuzel, O.: Human body tracking by adaptive background models and mean-shift analysis. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Nice, France (2003)
Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition, Readings in speech recognition, pp. 267–296. Morgan Kaufmann Publishers Inc, San Francisco (1990)
Shechtman, E., Irani, M.: Space-Time Behavior Based Correlation. In: IEEE International Conference on Computer Vision and Pattern Recognition 2005, San Diego, CA, USA, pp. 405–412. IEEE, Los Alamitos (2005)
Starner, T., Weaver, J., Pentland, A.: Real time American sign language recognition from video using HMMs. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 1371–1375 (1998)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, Ft. Collins, USA, pp. 246–252. IEEE, Los Alamitos (1999)
Wang, J.J., Singh, S.: Video Analysis of Human Dynamics - a survey. Real-time Imaging Journal 9, 320–345 (2003)
Yamato, J., Ohya, J., Ishii, K.: Recognizing Human Action in Time-Sequential Images using Hidden Markov Models. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 379–385. IEEE, Los Alamitos (1992)
Zelnik-Manor, L., Irani, M.: Event based analysis of video. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 123–130. IEEE, Los Alamitos (2001)
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Achard, C., Qu, X., Mokhber, A., Milgram, M. (2007). Action Recognition with Semi-global Characteristics and Hidden Markov Models. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_25
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DOI: https://doi.org/10.1007/978-3-540-74607-2_25
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