A CNN-based probability hypothesis density filter for multitarget tracking

Recently, the probability hypothesis density filter (PHD) shows excellent multiple targets tracking performance, and it has been applied for tracking targets in video. The PHD filter usually needs to integrate other feature for image object tracking. However, the single hand-crafted feature shows poor robustness while utilizing multiple features fusion will increase the complexity. To alleviate the above problems, a deep convolutional neural networks (CNN) based PHD filter is proposed in this paper. The proposed method utilizes the impressive representability of the CNN feature to improve the robustness without increasing the complexity. Besides this, we also revise the update process of the standard PHD filter to output the continuous track and new birth targets, directly. The experiment tested on MOT17 dataset validate the efficacy of the proposed method in multitarget tracking in image sequences.