1. Object Pre-localization with Composite Filtering

1. Detecting Human Action in Active Video Hao Jiang, Ze-Nian Li and Mark S. DrewSchool of Computing Science, Simon Fraser University, Vancouver BC, Canada V5A 1S6 We propose a novel scheme to detect human actions in active video. Active videos are shot purposively, similar to the world seen from peoples’ eyes, and are object and action oriented, usually involving complex camera motions. We detect complex human actions in such videos. Introduction 3. Detail matching using linear programming In detail matching, we would like to find the point-to-point matching from template object to target object. The matching problem can be relaxed into a linear program: Experimental Results Finding action in a staged surveillance video: Finding actions in hockey games: Fig. 10. Shortlist of hockey players for shooting action. Target-template Alignment, and Measure of Similarity Object Matching Template Target Action Detection Fig. 4. Object detail matching with linear programming. Template Fig. 11. Finding another action in hockey games. Fig. 1. Detecting actions in videos. Fig. 6. Finding action in an indoor active video. 1.Object Pre-localization with Composite Filtering 2. Finding object correspondence in successive frames This problem is formulated as an object labeling problem and solved by BP. Method Comp- osite Temp- late Fig. 7. Object pre-location and correspondence. Fig. 12. Shortlists of hockey players for action in Fig.11. Local valleys Conclusion • We propose a novel three-step human action detection scheme for active videos. • Detects specific human actions by matching templates to video sequences, using a linear programming method. • Successfully applied in general videos and TV hockey games. • In future work we will study fusing other clues for action event detection, such as camera motions. To improve the approximation, we use the following successive convexification scheme: Fig. 2. Object matching with composite filtering. Delaunay Triangulation of feature points on template images For each site, set initial trust region to same size as entire target image Fig. 8. Hockey player trajectories. Calculate matching costs for all possible candidate target points Find lower convex hull vertices in trust regions, and target point basis sets Update trust regions Build and solve LP relaxation Update control points Trust region small? No For further information Please contact {hjiangb, li, mark}@cs.sfu.edu. Output results Yes Fig. 5. Diagram of successive convexification Fig. 3. Object correspondence as a labeling problem. Fig. 9. Finding shooting action.