Group Action Recognition Using Space-Time Interest Points

Authors:
Qingdi Wei;Xiaoqin Zhang;Yu Kong;Weiming Hu;Haibin Ling
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, P.R. China 100190;National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, P.R. China 100190;Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, P.R. China 100081;National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, P.R. China 100190;Center for Information Science and Technology, Computer and Information Science Department, Temple University, Philadelphia, USA
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Year:
2009

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Abstract

Group action recognition is a challenging task in computer vision due to the large complexity induced by multiple motion patterns. This paper aims at analyzing group actions in video clips containing several activities. We combine the probability summation framework with the space-time (ST) interest points for this task. First, ST interest points are extracted from video clips to form the feature space. Then we use k-means for feature clustering and build a compact representation, which is then used for group action classification. The proposed approach has been applied to classification tasks including four classes: badminton, tennis, basketball, and soccer videos. The experimental results demonstrate the advantages of the proposed approach.