Feature Point Correspondence in the Presence of Occlusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Recognition of Periodic, Nonrigid Motion
International Journal of Computer Vision
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Extraction and Temporal Segmentation of Multiple Motion Trajectories in Human Motion
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
Temporal spatio-velocity transform and its application to tracking and interaction
Computer Vision and Image Understanding - Special issue on event detection in video
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Detection of Fence Climbing from Monocular Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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Human motion analysis is one of the important topics in visual surveillance applications,the ultimate goal of which is to achieve automated scene understanding. This paper proposes a novel "stable contact "concept for temporal abstraction of image sequences, and presents a Hidden Markov Model (HMM)based framework to recognize continuous human activities. With the extended star-skeleton representation, stable contacts are formed by stationary extreme points, and image sequences are segmented temporally into adjacent but disjoint primitive intervals. We define a set of primitive motion units (PMU 's)over primitive intervals based on stable contacts and trajectories. Thus frame sequences are abstracted as PMU sequences. Discrete HMM 's are trained on manually segmented sequences to classify segmented testing PMU sequences into predefined activities. The continuous recognition on non-segmented PMU sequences is achieved by searching over the time axis, for the best fit between durations of PMU sequences and types of activities. The experiments on various sequences of (mixed) human activities, including walking, running and climbing (fences or rocks), are presented to show the effectiveness of the proposed concept and framework.