# New Performance Measures for Object Tracking under Complex Environments

arXiv: https://arxiv.org/abs/2111.07145 · HTML: https://mlsanity.com/paper/2111.07145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Unveiling the Power of Deep Tracking](https://mlsanity.com/paper/1804.06833/) (2018)
- [Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking](https://mlsanity.com/paper/1807.11348/) (2018)
- [A Survey of Appearance Models in Visual Object Tracking](https://mlsanity.com/paper/1303.4803/) (2013)
- [Visual object tracking performance measures revisited](https://mlsanity.com/paper/1502.05803/) (2015)

## 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.