Algorithm for Image Object Recognition and Tracking in Complex Backgrounds

This article mainly studies the image object recognition and tracking algorithm for complex backgrounds, to improve the performance of object recognition and tracking in complex scenes such as dynamic change, uneven illumination and severe occlusion. To achieve this goal, a comprehensive algorithm framework is proposed. This method combines the feature extraction ability of DL (Deep learning), focusing effect of attention mechanism, adaptive motion model and flexible updating strategy. In the object recognition experiment, the proposed algorithm shows significant advantages, especially when dealing with illumination changes and occlusion problems, where the detection accuracy has been significantly improved. Furthermore, the algorithm shows stable tracking performance and a high tracking success rate in the target tracking experiment. It can keep continuous tracking of the target even in complex situations such as background interference and interaction between targets. These results fully verify the effectiveness and robustness of the algorithm. It is concluded that the image object recognition and tracking algorithm for complex background proposed in this article has achieved remarkable results in many aspects, contributing to the development of image processing and computer vision technology.