Robust False Positive Detection for Real-Time Multi-target Tracking

We present a real-time multi-target tracking system that effectively deals with false positive detections. In order to achieve this, we build a novel motion model that treats false positives on background objects and false positives on foreground objects such as shoulders or bags separately. In addition we train a new head detector based on the Aggregated Channel Features (ACF) detector and propose a schema that includes the identification of true positives with the data association instead of using the internal decision-making process of the detector. Through several experiments, we show that our system is superior to previous work.