Unsupervised multiple object tracking on video with no ego motion

Abstract: Multiple-object tracking is a task within the field of computer vision. As the name stated, the task consists of tracking multiple objects in the video, an algorithm that completes such task are called trackers. Many of the existing trackers require supervision, meaning that the location and identity of each object which appears in the training data must be labeled. The procedure of generating these labels, usually through manual annotation of video material, is highly resource-consuming. On the other hand, different from well-known labeled Multiple-object tracking datasets, there exist a massive amount of unlabeled video with different objects, environments, and video specifications. Using such unlabeled video can therefore contribute to cheaper and more diverse datasets. There have been numerous attempts on unsupervised object tracking, but most rely on evaluating the tracker performance on a labeled dataset. The reason behind this is the lack of an evaluation method for unlabeled datasets. This project explores unsupervised pedestrian tracking on video taken from a stationary camera over a long duration. On top of a simple baseline tracker, two methods are proposed to extend the baseline to increase its performance. We then propose an evaluation method that works for unlabeled video, which we use to evaluate the proposed methods. The evaluation method consists of the trajectory completion rate and the number of ID switches. The trajectory completion rate is a novel metric proposed for pedestrian tracking. Pedestrians generally enter and exit the scene for video taken by a stationary camera in specific locations. We define a complete trajectory as a trajectory that goes from one area to another. The completion rate is calculated by the number of complete trajectories over all trajectories. Results showed that the two proposed methods had increased the trajectory completion rate on top of the original baseline performance. Moreover, both proposed methods did so without significantly increasing the number of ID switches.