Improved Tracking Algorithm Based on Motion Trend Prediction

Target tracking technology plays a crucial role in the field of computer vision, particularly in complex scenarios such as autonomous driving, video surveillance, human-computer interaction, and virtual reality, where demand continues to grow. However, target tracking tasks face a series of challenges, including but not limited to frequent target occlusions, uncertainty in trajectory initialization and termination, similarity in appearance features, and interference from complex backgrounds. This paper addresses the challenges of occlusion, interference from similar targets, and changes in target shape in complex scenes by proposing an improved target tracking algorithm that incorporates motion trend prediction. The algorithm combines a Deep Residual Shrinkage Network (DRSN) for appearance feature extraction and non-linear motion model prediction. Through channel-wise soft thresholding with different thresholds, it removes redundant information and precisely extracts the appearance features of the target. Experimental results show that the proposed algorithm improves both the accuracy and speed of target tracking, particularly excelling in occlusion recovery and target reidentification.