In this paper, we present HOOT, the Heavy Occlusions in Object Tracking Benchmark, a new visual object tracking dataset aimed towards handling high occlusion scenarios for single-object tracking tasks. The dataset consists of 581 high-quality videos, which have 436K frames densely annotated with rotated bounding boxes for targets spanning 74 object classes. The dataset is geared for development, evaluation and analysis of visual tracking algorithms that are robust to occlusions. It is comprised of videos with high occlusion levels, where the median percentage of occluded frames per-video is 68%. It also provides critical attributes on occlusions, which include defining a taxonomy for occluders, providing occlusion masks for every bounding box, per-frame partial/full occlusion labels and more. HOOT has been compiled to encourage development of new methods targeting occlusion handling in visual tracking, by providing training and test splits with high occlusion levels. This makes HOOT the first densely-annotated, large dataset designed for single-object tracking under severe occlusion. We evaluate 15 state-of-the-art trackers on this new dataset to act as a baseline for future work focusing on occlusions.

Occlusion handling is one of the important challenges in the field of visual tracking, especially for real-time applications, where further processing for occlusion reasoning may not always be possible. In this paper, an occlusion-aware real-time object tracker is proposed, which enhances the baseline SiamRPN model with an additional branch that directly predicts the occlusion level of the object. Experimental results on GOT-10k and VOT benchmarks show that learning to predict occlusion levels end-to-end in this multi-task learning framework helps improve tracking accuracy, especially on frames that contain occlusions. Up to 7% improvement on EAO scores can be observed for occluded frames, which are only 11% of the data. The performance results over all frames also indicate the model does favorably compared to the other trackers.