Abstract

This paper presents a novel approach for multi-person track- ing 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 hu- man 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 sub- jects with different motion velocity and is modeled as a static-dynamic attention map. With the static-dynamic attention map and retinal map- ping, 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 sub jects in complex scenes and the evaluation on public datasets show promising improvements over state-of-the-art methods.