High-Speed Action Recognition and Localization in Compressed Domain Videos

We present a compressed domain scheme that is able to recognize and localize actions in real-time1 . The recognition problem is posed as performing an action video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion directions and magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a large action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our classification results compare favorably with existing methods at only a fraction of their computational cost. We also perform a systematic investigation of the effects of various encoding options on our proposed approach. In particular, we present results on the co...

• Type: Journal
• Year: 2008
• Where: TCSV
• Authors: Chuohao Yeo, Parvez Ahammad, Kannan Ramchandran, Shankar Sastry
• Keywords: Action Video, Action Video Query, Compressed Domain Scheme, TCSV 2008