I. Introduction
Robust tracking of multiple objects [1] is a challenging problem in computer vision and acts as an important component of many real-world applications. It aims to reliably recover trajectories and maintain identities of objects of interest in an image sequence. State-of-the-art Multi-Object Tracking (MOT) methods [2], [3] mostly utilize the tracking-by-detection strategy because of its robustness against tracking drift. Such a strategy generates per-frame object detection results from the image sequence and associates the detections into object trajectories. It is able to handle newly appearing objects and is robust to tracking drift. The tracking-by-detection methods can be categorized into offline and online methods. The offline methods [4] use both detection results from past and future with some global optimization techniques for linking detections to generate object trajectories. The online methods, on the other hand, use only detection results up to the current time to incrementally generate object trajectories. Our proposed method focuses on online MOT, which is more suitable for real-time applications including autonomous driving and intelligent surveillance.