Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)

This paper aims to research and implement a real-time video target tracking algorithm based on Convolutional Neural Networks (CNN), enhancing the accuracy and robustness of target tracking in complex scenarios. Addressing the limitations of traditional tracking algorithms in handling issues such as target occlusion, morphological changes, and background interference, our approach integrates target detection and tracking strategies. It continuously updates the target model through an online learning mechanism to adapt to changes in the target's appearance. Experimental results demonstrate that, when dealing with situations involving rapid motion, partial occlusion, and complex backgrounds, the proposed algorithm exhibits higher tracking success rates and lower failure rates compared to several mainstream tracking algorithms. This study successfully applies CNN to real-time video target tracking, improving the accuracy and stability of the tracking algorithm while maintaining high processing speeds, thus meeting the demands of real-time applications. This algorithm is expected to provide new solutions for target tracking tasks in video surveillance and intelligent transportation domains.