Laura Leal-Taix´e, Gerard Pons-Moll and Bodo Rosenhahn ICCV2011

1. Everybody needs somebody: Modeling social and grouping behavior on a linearprogramming multiple people tracker Laura Leal-Taix´e, Gerard Pons-Moll and Bodo Rosenhahn ICCV2011

2. Outline • Goal • Multiple people tracking • Modeling social behavior • Experimental results • Conclusion

3. Goal • People detection is not always correct. • It is important to merge the detection results into right trajectoies.

4. Multiple people tracking • divided in two steps • object detection • data associationform complete trajectories • Build a graph with the nodes pedestrian detections • The matching problem is equivalent to minimum-cost network flow problem

5. Multiple people tracking • ,trajectory of k • Find the that best explains the detection. • 4 • P(oi|T) is the likelihood.

6. Multiple people tracking • trajectory Tk have following dependencies • Constant velocity assumption find oi depends on oi-1,oi-2 • Grouping behavior • Avoidance term

7. Multiple people tracking • Represent by Markov chain:

8. Multiple people tracking

9. Multiple people tracking • Combine (1),(2),(3)

10. Multiple people tracking • Three kinds of edges: • Link edges • Detection edges • Entrance and exit edges

11. Multiple people tracking • Link edges • The edges (ei, bj) connect the end nodes eiwith the beginning nodes bjin following frames,with cost Ci,j and flag fi,j • Flag =1 if oi and oj belong to Tk,and ∆f≤Fmax • 111

12. Multiple people tracking • Detection edges • The edges (bi, ei) connect the beginning node bi and end node ei, with cost Ci and flag fi

13. Modeling social behavior • If a pedestrian doesn’t meet any obstacles, he will naturally follow a straight line. • But the pedestrian will have some social behavior. • Add Social Force Model (SFM)and Group behavior(GR) into the problem.

14. Modeling social behavior • Social forces have three main terms: • The desire to maintain certain speed • The desire to keep away from others • The desire to reach a destination • We focus on first two!

15. Modeling social behavior • Constant velocity assumpion • When a person walk at a speed V at time t • We assume he will have speed V at time t+∆t

16. Modeling social behavior • Avoidance term

17. Modeling social behavior • From the training sequence in [22] , we learn the probabilty of Pg and Pi [22] S. Pellegrini, A. Ess, K. Schindler, and L. van Gool. You’ll never walk alone: modeling social behavior for multi-target tracking. ICCV, 2009. 1, 2, 5, 7

18. Experimental results Blue=>DIST Greed=>with SDM Red=>SFM+GR

19. Experimental results

20. Experimental results • To show the importance of social behavior and the robustness of our algorithm at low frame rates, we track at 2.5fps (taking one every tenth frame).

21. Experimental results • DA (detection accuracy) • TA (tracking accuracy) • DP (detection precision) • TP (tracking precision)

22. Experimental results [28]use network flow [22]use social behavior [27] use social and grouping

23. Experimental results

24. Conclusion • It is important to have social and group relation on tracking. • This paper outperform on low fps than othersand have high accuracies on miss detections,false alarms and noise.