Optical Flow Method for Target Tracking and Segmentation

This paper presents an innovative approach using optical flow method for target tracking and segmentation. This computer vision-based technique plays a crucial role in high-precision simulation and modeling scenarios. The optical flow method is a prominent computer vision technique that analyzes pixel movement across image sequences to infer motion patterns within scenes. For target tracking and segmentation tasks, optical flow enables more accurate target localization and identification, thereby enhancing task success rates. Implementation typically involves calculating displacement vectors between consecutive frames using algorithms like Lucas-Kanade or Horn-Schunck methods, which compute optical flow fields through gradient-based approaches. Key functions often include motion vector computation, feature point correspondence matching, and segmentation based on motion coherence. By employing optical flow for target tracking and segmentation, significant improvements can be achieved in high-precision simulation and modeling applications, particularly through motion-based foreground extraction and trajectory prediction capabilities.