Know Your Surroundings: Exploiting Scene Information for Object Tracking
This addresses the challenge of robust object tracking in computer vision, particularly for applications like surveillance and autonomous driving, by introducing a novel method that integrates scene context, representing an incremental advance over appearance-only trackers.
The paper tackles the problem of object tracking by exploiting scene information to improve robustness against appearance changes and distractors, achieving a state-of-the-art AO score of 63.6% on the GOT-10k dataset.
Current state-of-the-art trackers only rely on a target appearance model in order to localize the object in each frame. Such approaches are however prone to fail in case of e.g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking. Having the knowledge about the presence and locations of other objects in the surrounding scene can be highly beneficial in such cases. This scene information can be propagated through the sequence and used to, for instance, explicitly avoid distractor objects and eliminate target candidate regions. In this work, we propose a novel tracking architecture which can utilize scene information for tracking. Our tracker represents such information as dense localized state vectors, which can encode, for example, if the local region is target, background, or distractor. These state vectors are propagated through the sequence and combined with the appearance model output to localize the target. Our network is learned to effectively utilize the scene information by directly maximizing tracking performance on video segments. The proposed approach sets a new state-of-the-art on 3 tracking benchmarks, achieving an AO score of 63.6% on the recent GOT-10k dataset.