1. Imran N. Junejo, Omar Javed and Mubarak Shah University of Central Florida, Proceedings of the 17th International Conference on Pattern Recognition, pp.716-719, Aug., 2004 Byung-Seok Woo bswoo@islab.ulsan.ac.kr August 21, 2010 Multi Feature Path Modeling for Video Surveillance

2. 2 Intelligent Systems Lab. Abstract Goal Learning the route or paths most commonly taken by objects as they traverse through a scene Registering any unusual activity A person traversing an area not traversed before A person moving at different speed than the usual A person moving in varying directions

3. 3 Intelligent Systems Lab. Training for Path Detection(1/3) Trajectories

4. 4 Intelligent Systems Lab. Training for Path Detection(2/3) Min-cut graph algorithm Node : trajectory Edge : weight - distance between two trajectories using Hausdorff distance ⇒ Each consisting of a group of similar trajectories

5. 5 Intelligent Systems Lab. Envelope the spatial extent of a path 1. correspondence between trajectories in a cluster 2. selecting the boundary points Correspondence - Obtained using the Dynamic time Warping (DTW) algorithm Average trajectory From the correspondences Training for Path Detection(3/3)

6. 6 Intelligent Systems Lab. Scene Model Path model distinguish between trajectories spatially dissimilar spatially similar but of different speeds crooked vs. straight To Register any unusual activity Three hierarchical steps Spatial Features Velocity Features Curvature Features

7. 7 Intelligent Systems Lab. Spatial Features The spatial location of the trajectory Compared to the paths already present in the database To determine spatial similarity two conditions 1. 90% of points in the test trajectory should lie with in the envelope of the path 2. The Hausdorff distance between the average path trajectory and the test trajectory the Hausdorff distance between path envelope boundaries. If) Not satisfied two conditions, the trajectory = anomalous

8. 8 Intelligent Systems Lab. Velocity Features To discriminate between trajectories of varying motion characteristics Velocity Mahalanobis distance

9. 9 Intelligent Systems Lab. Curvature Features Curvature of the trajectory Comparing the curvature of the test trajectory with predefined distribution using the Mahalanobis distance

10. 10 Intelligent Systems Lab. Results (a) bicyclist (b) d runkard walking (c) walking (d) running

11. 11 Intelligent Systems Lab. Conclusion Path detection through trajectory Unusual behavior detection using spatial, velocity and curvature features My opinion… Need to a variety of trajectory database for path detection Noise by any animal