HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

Author:

Luiten JonathonORCID,Os̆ep Aljos̆a,Dendorfer Patrick,Torr Philip,Geiger Andreas,Leal-Taixé Laura,Leibe Bastian

Abstract

AbstractMulti-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics overemphasize the importance of either detection or association. To address this, we present a novel MOT evaluation metric, higher order tracking accuracy (HOTA), which explicitly balances the effect of performing accurate detection, association and localization into a single unified metric for comparing trackers. HOTA decomposes into a family of sub-metrics which are able to evaluate each of five basic error types separately, which enables clear analysis of tracking performance. We evaluate the effectiveness of HOTA on the MOTChallenge benchmark, and show that it is able to capture important aspects of MOT performance not previously taken into account by established metrics. Furthermore, we show HOTA scores better align with human visual evaluation of tracking performance.

Funder

European Research Council

Google

Alexander von Humboldt-Stiftung

CCAV

Engineering and Physical Sciences Research Council

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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