<p>Visual tracking is a complex and crucial problem in computer vision with numerous real-world applications, including surveillance, autonomous vehicles, and augmented reality. To tackle the challenges associated with tracking performance, this paper presents Learning Disruptor-Aware Channel Selection and Reliability with Target Regularization (DACSR). First, DACSR enhances tracking robustness in challenging scenarios by adaptively selecting visual channels based on their resistance to disruptions through an intelligent disruption-aware channel selection mechanism. Second, it improves predictive accuracy and reliability by integrating a channel stability-aware normalization method, which highlights stable channels while suppressing misleading ones during optimization. Third, this study incorporates target regularization techniques using deep neural networks to capture target-specific characteristics beyond spatial considerations, further refining the tracking process. By leveraging the representational power of deep neural networks, DACSR effectively models complex target attributes, leading to improved tracking accuracy. Finally, we validate the efficiency and robustness of the proposed method through extensive experiments on benchmark datasets, demonstrating its superior performance over existing approaches in challenging tracking scenarios.</p>