Learning to Track Any Object
It addresses the challenge of tracking any object without category-specific priors, offering a unified detection and tracking approach, though it builds incrementally on existing detector methods.
The paper tackles the problem of class-agnostic object tracking by repurposing category-specific object detectors into a generic objectness prior, achieving state-of-the-art results on benchmarks like OxUvA and GOT, and producing high-quality instance segmentation masks on DAVIS '17.
Object tracking can be formulated as "finding the right object in a video". We observe that recent approaches for class-agnostic tracking tend to focus on the "finding" part, but largely overlook the "object" part of the task, essentially doing a template matching over a frame in a sliding-window. In contrast, class-specific trackers heavily rely on object priors in the form of category-specific object detectors. In this work, we re-purpose category-specific appearance models into a generic objectness prior. Our approach converts a category-specific object detector into a category-agnostic, object-specific detector (i.e. a tracker) efficiently, on the fly. Moreover, at test time the same network can be applied to detection and tracking, resulting in a unified approach for the two tasks. We achieve state-of-the-art results on two recent large-scale tracking benchmarks (OxUvA and GOT, using external data). By simply adding a mask prediction branch, our approach is able to produce instance segmentation masks for the tracked object. Despite only using box-level information on the first frame, our method outputs high-quality masks, as evaluated on the DAVIS '17 video object segmentation benchmark.