mlsanityData to 7 Oct 2026

New Performance Measures for Object Tracking under Complex Environments

arXiv 2111.07145 ↗First posted 13 Nov 2021cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 22 references that are in the map. First posted on arXiv 13 Nov 2021.

Builds on

4 of 22 references.

References · 4

  1. 2018Unveiling the Power of Deep Trackingcited by 41
  2. 2018Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Trackingcited by 25
  3. 2013A Survey of Appearance Models in Visual Object Trackingcited by 21
  4. 2015Visual object tracking performance measures revisitedcited by 12

Built on by

Not cited yet.