Behavior monitoring through automatic analysis of video sequences

This paper addresses the problem of classifying actions performed by a human subject in a video sequence. A representation eigenspace approach based on the visual appearance is used to train the classifier. Before dimensionality reduction exploiting the PCA/LLE algorithms, a high dimensional description of each frame of the video sequence is constructed, based on foreground blob analysis. The classification task is performed by matching incrementally the reduced representation of the test image sequence against each of the learned ones, and accumulating matching scores until a decision is obtained. Experimental results demonstrate that the approach is accurate enough and feasible for behavior classification. Categories and Subject Descriptors I.2.10 [Artificial Intelligence]: vision and scene understanding—video analysis Keywords Visual surveillance, Behavior classification

• Type: Conference
• Year: 2007
• Where: CIVR
• Authors: Carlo Colombo, Dario Comanducci, Alberto Del Bimbo
• Keywords: Behavior Classification, CIVR 2007, Representation Eigenspace Approach, Video Sequence