基于场景变换目标稳定跟踪系统OA

To address the problem of cross-scene object tracking,this paper proposes a scene transformation-based algorithm for stable object tracking.By integrating a feature enhancement module and an object refinement module into the Kernelized Correlation Filter(KCF)algorithm,the contour and detailed information of the target are highlighted.Meanwhile,the U-Net neural network is intro-duced to accurately detect scene boundaries,and a dynamic update and target reacquisition strategy is incorporated into the decision-making framework to ensure stable object tracking.Given that the proposed algorithm fails to meet real-time performance requirements due to increased computational complexity,the Atlas 300I edge computing module is adopted for algorithm acceleration.This approach effectively balances the trade-off between algorithm complexity and tracking performance,improving tracking accuracy while satisfying the real-time constraints of practical engineering applications.The experimental results demonstrate that the proposed algorithm can effectively resolve the issue of tracking drift in cross-scene object tracking scenarios,providing valuable insights for future research in this field.