Opening up Open-World Tracking
This work addresses the evaluation deficit for autonomous systems that need to track never-seen-before objects, which is crucial for safety but currently lacks standardized benchmarks.
The paper tackles the problem of evaluating tracking and detection of both known and unknown objects in open-world settings by proposing a new benchmark, TAO-OW, to enable standardized comparisons and advance research in this area.
Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is blind to never-seen-before objects poses a safety hazard when operating in the real world - and yet this is how almost all current systems work. One of the main obstacles towards advancing tracking any object is that this task is notoriously difficult to evaluate. A benchmark that would allow us to perform an apples-to-apples comparison of existing efforts is a crucial first step towards advancing this important research field. This paper addresses this evaluation deficit and lays out the landscape and evaluation methodology for detecting and tracking both known and unknown objects in the open-world setting. We propose a new benchmark, TAO-OW: Tracking Any Object in an Open World, analyze existing efforts in multi-object tracking, and construct a baseline for this task while highlighting future challenges. We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world. https://openworldtracking.github.io/