Everybody needs somebody: Modeling social and grouping behavior on a linear

programming multiple people trackerLaura Leal-Taix´e, Gerard Pons-Moll and

Bodo RosenhahnICCV2011

Outline

• Goal• Multiple people tracking• Modeling social behavior• Experimental results• Conclusion

Goal

• People detection is not always correct.• It is important to merge the detection results

into right trajectoies.

Multiple people tracking

• divided in two steps– object detection– data associationform complete trajectories

• Build a graph with the nodes pedestrian detections

• The matching problem is equivalent to minimum-cost network flow problem

Multiple people tracking

• ,trajectory of k• Find the that best explains the

detection.• 4

• P(oi|T) is the likelihood.

Multiple people tracking

• trajectory Tk have following dependencies– Constant velocity assumption find oi depends on oi-1,oi-2

– Grouping behavior – Avoidance term

Multiple people tracking

• Represent by Markov chain:

Multiple people tracking

Multiple people tracking

• Combine (1),(2),(3)

Multiple people tracking

• Three kinds of edges:– Link edges– Detection edges– Entrance and exit edges

Multiple people tracking

• Link edges• The edges (ei, bj) connect the end nodes ei

with the beginning nodes bj in following frames,with cost Ci,j and flag fi,j

• Flag =1 if oi and oj belong to Tk,and ∆f≤Fmax

• 111

Multiple people tracking

• Detection edges• The edges (bi, ei) connect the beginning node

bi and end node ei, with cost Ci and flag fi

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.

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!

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

Modeling social behavior

• Avoidance term

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

Experimental results

Blue=>DISTGreed=>with SDMRed=>SFM+GR

Experimental results

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).

Experimental results

• DA (detection accuracy)• TA (tracking accuracy)• DP (detection precision)• TP (tracking precision)

Experimental results

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

Experimental results

Conclusion

• It is important to have social and group relation on tracking.

• This paper outperform on low fps than others and have high accuracies on miss detections,false alarms and noise.