What Do I See? Modeling Human Visual Perception for Multi-person Tracking^*

Xu Yan, Ioannis A. Kakadiaris, and Shishir K. Shah

Abstract. This paper presents a novel approach for multi-person tracking utilizing a model motivated by the human vision system. The model predicts human motion based on modeling of perceived information. An attention map is designed to mimic human reasoning that integrates both spatial and temporal information. The spatial component addresses human attention allocation to different areas in a scene and is represented using a retinal mapping based on the log-polar transformation while the temporal component denotes the human attention allocation to subjects with different motion velocity and is modeled as a static-dynamic attention map. With the static-dynamic attention map and retinal mapping, attention driven motion of the tracked target is estimated with a center-surround search mechanism. This perception based motion model is integrated into a data association tracking framework with appearance and motion features. The proposed algorithm tracks a large number of subjects in complex scenes and the evaluation on public datasets show promising improvements over state-of-the-art methods.

^*This work was supported in part by the US Department of Justice, grant number 2009-MU-MU-K004. Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of our sponsors.

LNCS 8690, p. 314 ff.

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