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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Ma, Y., Damelin, S. B., Masoud, O., & Papanikolopoulos, N. (2006). Activity recognition via classification constrained diffusion maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4291 LNCS-I, pp. 1–8). Springer Verlag. https://doi.org/10.1007/11919476_1
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