Target Tracking Algorithm Incorporating Saliency Technology

With the development of video surveillance and mobile robotics, target tracking plays a crucial role in real-time video analysis. In order to improve the performance and robustness of tracking algorithms in complex environments, this study proposes a new target tracking algorithm that enhances target identification and localization by incorporating saliency detection techniques. We first use a deep learning approach to train a saliency detection model that accurately identifies and highlights potential target regions in video frames. In this paper, we combine the traditional correlation filter tracking framework to effectively fuse the saliency map with the original video frames to improve the accuracy and adaptability of tracking. On the basis of this, this paper proposes a new target detection method, on the basis of which, the target tracking accuracy and adaptive ability are further improved by extracting the target image. In the experiments with indoor fixed cameras, the success rates of tracking are 80% and 75% before fusion; after fusion, the success rate reaches 90% and the correct rate reaches 88%. The research results in this paper will help to improve the practicality and effectiveness of target tracking methods and provide new ideas and application examples for mining saliency information in complex scenarios.