The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Probabilistic Motion Parameter Models for Human Activity Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Recognition and Interpretation of Parametric Gesture
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Inference of Human Postures by Classification of 3D Human Body Shape
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
View-invariant modeling and recognition of human actions using grammars
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
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Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous approaches on activity recognition focus on recognition from a single view and ignore the issue of view invariance, and they deal with recognizing a single activity. There are only few published algorithms for segmenting and recognizing complex activities that are composed of more than one activity. In this paper, we present a view invariant human activity recognition approach that uses both motion and shape information. An augmented vector of both optical flow features as well as eigen shape features is used to represent motion and shape of the body in the region of interest in each frame of the sequence. Each activity is represented by a set of hidden Markov models, where each model represents the activity from a different viewing direction, to realize the view invariance. Also, we present a voting-based approach to automatically and effectively segment and recognize complex activities. Experiments on two sets of video clips of different activities show that our method is effective.