Object tracking based on non-local means denoising technique and balanced window penalty function

Ke Wang; Zhiyong An

doi:10.1117/12.3049568

8 November 2024 Object tracking based on non-local means denoising technique and balanced window penalty function

Ke Wang, Zhiyong An

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162A (2024) https://doi.org/10.1117/12.3049568
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Visual object tracking is a critical and complex task in computer imagery. However, most Transformer-based tracking models do not consider the characteristics of tracking images, often being affected by various noises in video sequences, leading to boundary box jitter and inaccurate predictions. To mitigate these challenges, this work presents a robust Transformer tracking technique that combines non-local means denoising technique and a balanced window penalty function. The non-local means denoising technique is introduced during the feature extraction network stage as a preprocessing step, removing noise while preserving image details as much as possible, thus providing cleaner and more reliable inputs for subsequent tracking tasks. Additionally, this paper introduces the balanced window penalty function, which enhances the accuracy of target position control by adjusting the weight distribution between the inside and outside of the window. Experiments on the OTB100, TNL2K, UAV123, GOT-10k and LaSOT_ext datasets validate that this method significantly improves tracking accuracy and success rates in various complex scenarios. Especially in challenging scenes involving fast motion, occlusion, and cluttered backgrounds, this method exhibits strong robustness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Ke Wang and Zhiyong An "Object tracking based on non-local means denoising technique and balanced window penalty function", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162A (8 November 2024); https://doi.org/10.1117/12.3049568

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KEYWORDS

Windows

Denoising

Transformers

Video

Feature extraction

Visualization

Detection and tracking algorithms

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