Opening up Open-World Tracking
Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave and, Deva Ramanan, Bastian Leibe, Aljo\v{s}a O\v{s}ep, Laura Leal-Taix\'e

TL;DR
This paper introduces a new benchmark, TAO-OW, for evaluating the ability of systems to detect and track both known and unknown objects in open-world scenarios, addressing a key challenge in autonomous system safety.
Contribution
It presents the first comprehensive evaluation methodology and benchmark for open-world object tracking, enabling fair comparison and progress in this emerging research area.
Findings
Analysis of existing multi-object tracking efforts
Construction of a baseline for open-world tracking
Identification of future challenges in the field
Abstract
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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Neural Network Applications · Air Quality Monitoring and Forecasting
