Face Tracking Based on Improved TLD Algorithm of Detection Module in Low Light Environment

The TLD (Tracking-Learning-Detection) algorithm acquires target features through continuous learning from the initial target, enabling prolonged target tracking. However, during the tracking process, variations in lighting conditions, occlusions, and target movements significantly affect its tracking performance. Consequently, the traditional TLD algorithm exhibits high complexity and computational time in its detection module. It also suffers from poor classification performance in low-light environments, inability to meet real-time and accuracy requirements, among others. To address these issues, this study proposes improvements to the detection module of the TLD algorithm. Specifically, we incorporate the LBP (Local Binary Patterns) texture features into the detection module of the TLD algorithm, utilizing them for sample classification to enhance the classification performance. Additionally, we employ the Markov algorithm to predict facial motion, reducing the detection area of the module and improving the algorithm’s real-time capabilities. Experimental results validate that the proposed algorithm demonstrates improved tracking accuracy and real-time performance.