An online learned hough forest model for multi-target tracking

We present an online learned framework for multiple target tracking in a crowded scene. The tracking problem is formulated as a detection-based progressive association task. Firstly, reliable tracklets are generated by low level constraints among detection responses. Then longer tracklets associations are generated based on online learned Hough forest framework which effectively combines motion and appearance information for discrimination between two tracklets. In online learning scene, the association is formulated as a MAP problem and training examples are collected based on spatial-temporal constraints. In order to alleviate the drifting problem of online learning, Hungarian algorithm is employed to modify associated errors and update the training set. The experimental results show the effectiveness of our approach.