First-person multiple object tracking in complex traffic scenes

In this paper, we study multi-object tracking problem from the first-person viewpoint, e.g., the moving camera. This problem is different from the traditional one with static camera and brings lots of challenges. To solve this problem, we adopt the tracking-by-detection approach and design a new similarity model for two detection responses considering the camera motion. The similarity model can handle the change of scale and position of objects under the movement of camera. We also consider the detection prior and appearance to improve the tracking performance. The final tracking problem is solved within a network flow framework. Experimental results on KITTI dataset demonstrate the advantages of our method.