Learning to Track Multi-target Online by Boosting and Scene Layout

We address two principal difficulties of multi-target tracking in a real traffic scenario. Firstly, fast moving traffic scenarios lead to large displacements and complex interactions with occlusions and ambiguities. Secondly, the tracking application for real traffic scenarios has the online requirement. To surmount these difficulties, we propose an approach to track the multi-target online by Boosting and scene context reasoning. To this end, we use a two-stage system, where the first stage learns a non-linear classifier which is capable of generating the observation similarities. In the second stage, we demonstrate a novel relationship between observations and the scene layout parameters. Using a probabilistic formulation and the above relationship, our method has the unique ability to handle exceptions. To evaluate our method, we create three real traffic data sets, covering urban, rural, and highway conditions. We hope that these datasets will push forward the performance of tracking systems when being moved outside the laboratory to the real world.