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The most common paradigm for vision-based multi-object tracking istracking-by-detection, due to the availability of reliable detectors forseveral important object categories such as cars and pedestrians. However,future mobile systems will need a capability to cope with rich human-madeenvironments, in which obtaining detectors for every possible object categorywould be infeasible. In this paper, we propose a model-free multi-objecttracking approach that uses a category-agnostic image segmentation method totrack objects. We present an efficient segmentation mask-based tracker whichassociates pixel-precise masks reported by the segmentation. Our approach canutilize semantic information whenever it is available for classifying objectsat the track level, while retaining the capability to track generic unknownobjects in the absence of such information. We demonstrate experimentally thatour approach achieves performance comparable to state-of-the-arttracking-by-detection methods for popular object categories such as cars andpedestrians. Additionally, we show that the proposed method can discover androbustly track a large variety of other objects.

Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking