New Performance Measures for Object Tracking under Complex Environments

TL;DR

This paper introduces three new performance measures and a combined metric to better evaluate object tracking algorithms in complex environments, addressing limitations of existing measures like ACLE and ATA.

Contribution

The paper proposes novel auxiliary performance measures and a combined metric tailored for complex tracking scenarios, improving evaluation accuracy.

Findings

Proposed measures outperform existing ones in complex environments.

Combined measure provides a more comprehensive evaluation.

Experimental results validate the effectiveness of the new metrics.

Abstract

Various performance measures based on the ground truth and without ground truth exist to evaluate the quality of a developed tracking algorithm. The existing popular measures - average center location error (ACLE) and average tracking accuracy (ATA) based on ground truth, may sometimes create confusion to quantify the quality of a developed algorithm for tracking an object under some complex environments (e.g., scaled or oriented or both scaled and oriented object). In this article, we propose three new auxiliary performance measures based on ground truth information to evaluate the quality of a developed tracking algorithm under such complex environments. Moreover, one performance measure is developed by combining both two existing measures ACLE and ATA and three new proposed measures for better quantifying the developed tracking algorithm under such complex conditions. Some examples and experimental results conclude that the proposed measure is better than existing measures to quantify one developed algorithm for tracking objects under such complex environments.