Activity recognition via classification constrained diffusion maps

Authors:
Yunqian Ma;S. B. Damelin;O. Masoud;N. Papanikolopoulos
Affiliations:
Honeywell labs, Honeywell International Inc., Minneapolis, MN;Institute for Mathematics and its Applications, University of Minnesota, S.E Minneapolis, MN;Artificial Intelligence, Vision and Robotics Lab, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Artificial Intelligence, Vision and Robotics Lab, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Year:
2006

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Abstract

Applying advanced video technology to understand human activity and intent is becoming increasingly important for video surveillance. In this paper, we perform automatic activity recognition by classification of spatial temporal features from video sequence. We propose to incorporate class labels information to find optimal heating time for dimensionality reduction using diffusion via random walks. We perform experiments on real data, and compare the proposed method with existing random walk diffusion map method and dual root minimal spanning tree diffusion method. Experimental results show that our proposed method is better.