New Performance Measures for Object Tracking under Complex Environments
This work addresses the need for more accurate performance evaluation in object tracking for researchers and practitioners, but it is incremental as it builds on existing measures.
The authors tackled the problem of evaluating object tracking algorithms in complex environments like scaled or oriented objects, where existing measures like ACLE and ATA can be confusing. They proposed three new auxiliary measures and a combined measure, showing through examples and experiments that it better quantifies tracking algorithms under such conditions.
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.