HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking This paper introduces HOTA (Higher Order Tracking Accuracy), a novel metric for evaluating multi-object tracking (MOT) performance. HOTA balances the effects of accurate detection, association, and localization into a single unified metric for comparing trackers. It decomposes into a family of sub-metrics that evaluate each of five basic error types separately, enabling clear analysis of tracking performance. HOTA is evaluated on the MOTChallenge benchmark and shown to capture important aspects of MOT performance not previously considered by established metrics. It also aligns better with human visual evaluation of tracking performance. HOTA is designed to provide a single score for tracker evaluation that fairly combines all aspects of tracking, evaluate long-term higher-order tracking association, and decompose into sub-metrics for analyzing different components of tracker performance. It addresses the limitations of previous metrics like MOTA and IDF1, which overemphasize detection or association accuracy. HOTA explicitly measures both types of errors and combines them in a balanced way, incorporating localization accuracy not present in MOTA or IDF1. HOTA is calculated at multiple localization thresholds and the final score is the average of the scores calculated at each threshold. It is also decomposable into separate detection accuracy (DetA) and association accuracy (AssA) scores, which are then combined as the geometric mean to form HOTA. This formulation ensures that both detection and association are evenly balanced, unlike many other tracking metrics. HOTA is further decomposable into detection recall/precision and association recall/precision components. Detection recall measures the percentage of ground-truth detections that have been correctly predicted, while detection precision measures the percentage of detection predictions that are correct. Association recall measures how well predicted trajectories cover ground-truth trajectories, while association precision measures how well predicted trajectories keep to tracking the same ground-truth trajectories. HOTA is also able to measure higher-order association and matching, which considers associations over multiple time-steps and allows for more accurate evaluation of long-term tracking performance. This is in contrast to first-order association and matching, which only consider associations over a single time-step. HOTA's ability to measure higher-order association and matching makes it a more comprehensive metric for evaluating MOT performance.HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking This paper introduces HOTA (Higher Order Tracking Accuracy), a novel metric for evaluating multi-object tracking (MOT) performance. HOTA balances the effects of accurate detection, association, and localization into a single unified metric for comparing trackers. It decomposes into a family of sub-metrics that evaluate each of five basic error types separately, enabling clear analysis of tracking performance. HOTA is evaluated on the MOTChallenge benchmark and shown to capture important aspects of MOT performance not previously considered by established metrics. It also aligns better with human visual evaluation of tracking performance. HOTA is designed to provide a single score for tracker evaluation that fairly combines all aspects of tracking, evaluate long-term higher-order tracking association, and decompose into sub-metrics for analyzing different components of tracker performance. It addresses the limitations of previous metrics like MOTA and IDF1, which overemphasize detection or association accuracy. HOTA explicitly measures both types of errors and combines them in a balanced way, incorporating localization accuracy not present in MOTA or IDF1. HOTA is calculated at multiple localization thresholds and the final score is the average of the scores calculated at each threshold. It is also decomposable into separate detection accuracy (DetA) and association accuracy (AssA) scores, which are then combined as the geometric mean to form HOTA. This formulation ensures that both detection and association are evenly balanced, unlike many other tracking metrics. HOTA is further decomposable into detection recall/precision and association recall/precision components. Detection recall measures the percentage of ground-truth detections that have been correctly predicted, while detection precision measures the percentage of detection predictions that are correct. Association recall measures how well predicted trajectories cover ground-truth trajectories, while association precision measures how well predicted trajectories keep to tracking the same ground-truth trajectories. HOTA is also able to measure higher-order association and matching, which considers associations over multiple time-steps and allows for more accurate evaluation of long-term tracking performance. This is in contrast to first-order association and matching, which only consider associations over a single time-step. HOTA's ability to measure higher-order association and matching makes it a more comprehensive metric for evaluating MOT performance.