TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking
This work addresses the need for application-specific performance evaluation in multi-object tracking, but it is incremental as it focuses on parameter selection for an existing metric.
The paper tackles the problem of evaluating multi-object tracking algorithms for different applications by using the TGOSPA metric, enabling users to compare and optimize performance for specific tasks like target tracking.
Multi-object tracking algorithms are deployed in various applications, each with different performance requirements. For example, track switches pose significant challenges for offline scene understanding, as they hinder the accuracy of data interpretation. Conversely, in online surveillance applications, their impact is often minimal. This disparity underscores the need for application-specific performance evaluations that are both simple and mathematically sound. The trajectory generalized optimal sub-pattern assignment (TGOSPA) metric offers a principled approach to evaluate multi-object tracking performance. It accounts for localization errors, the number of missed and false objects, and the number of track switches, providing a comprehensive assessment framework. This paper illustrates the effective use of the TGOSPA metric in computer vision tasks, addressing challenges posed by the need for application-specific scoring methodologies. By exploring the TGOSPA parameter selection, we enable users to compare, comprehend, and optimize the performance of algorithms tailored for specific tasks, such as target tracking and training of detector or re-ID modules.