Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
Mustansar Fiaz, Arif Mahmood, Soon Ki Jung

TL;DR
This paper reviews recent object tracking algorithms, categorizes them, and evaluates their robustness against additive white Gaussian noise, revealing that noise significantly impacts tracking performance and highlighting the need for noise-robust designs.
Contribution
It provides a comprehensive survey of tracking algorithms and experimentally assesses their robustness to noise, an aspect previously underexplored in the field.
Findings
Performance degradation varies among algorithms under noise.
No tracker maintains high accuracy in noisy conditions.
Robustness to noise should be a key evaluation criterion.
Abstract
Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. In this paper, we aim to extensively review the latest trends and advances in the tracking algorithms and evaluate the robustness of trackers in the presence of noise. The first part of this work comprises a comprehensive survey of recently proposed tracking algorithms. We broadly categorize trackers into correlation filter based trackers and the others as non-correlation filter trackers. Each category is further classified into various types of trackers based on the architecture of the tracking mechanism. In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise.…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Impact of Light on Environment and Health
