Generic Object Crowd Tracking by Multi-Task Learning

We address Multiple Object Tracking (MOT) in crowds, where the type of target objects is generic and not limited to pedestrians as in most previous work. Following the popular tracking-by-detection strategy, we decompose this problem into two main tasks, detection and tracking, and formulate them under the Multiple Task Learning (MTL) framework. A binary detector is learnt to detect objects in images, whilst multiple trackers are learnt on top of the detector by MTL to trace detected objects in subsequent frames. The detector is utilised to anchor the trackers, helping them not drift away from targets. The trackers are jointly learnt by sharing common features. To further improve the performance, we use a smoothness term which considers all labelled and unlabelled data globally. Experiments on challenging new generic object sequences as well as a publicly available sequence show that the proposed method significantly outperforms the state-of-the-art methods.