Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker

Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Most tracking methods make the assumption that each pedestrian´s motion is independent, thereby ignoring the complex and important interaction between subjects. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, considering social and grouping behavior, and second, using a global optimization scheme to solve the data association problem. Results on three challenging publicly available datasets show our method outperforms state-of-the-art tracking systems.