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

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.