Activity Recognition Via Classification Constrained Diffusion Maps

  • Yunqian Ma
  • S. B. Damelin
  • O. Masoud
  • N. Papanikolopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


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.


Random Walk Dimensionality Reduction Video Sequence Minimal Span Tree Spectral Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yunqian Ma
    • 1
  • S. B. Damelin
    • 2
  • O. Masoud
    • 3
  • N. Papanikolopoulos
    • 3
  1. 1.Honeywell labs, Honeywell International Inc.MinneapolisUSA
  2. 2.Institute for Mathematics and its ApplicationsUniversity of MinnesotaS.E MinneapolisUSA
  3. 3.Artificial Intelligence, Vision and Robotics Lab, Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA

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