改进MeanShift算法在智能监控视频中的应用研究

Research on Application of Improved MeanShift Algorithm in Intelligent Surveillance Video

摘要: 智能视频监控是公共安全、交通管理、园区安防等领域的关键技术，运动目标实时跟踪是其核心环节。传统MeanShift算法具有计算简单、实时性强、抗部分遮挡等优势，但在复杂监控场景下，面对目标快速运动、尺度变化、背景干扰及光照波动，易出现跟踪漂移、目标丢失和误差累积等问题，跟踪鲁棒性不足。为此，本文提出一种特征匹配辅助初始化的改进MeanShift跟踪算法。该算法通过特征匹配快速定位目标运动区域，经形态学处理获取目标形心并动态生成迭代初始位置，再结合加权核直方图建模与Bhattacharyya相似度度量实现目标精准跟踪。实验结果表明，改进算法有效抑制误差累积，显著提升快速运动目标跟踪稳定性，单帧耗时稳定，可满足智能视频监控实时、鲁棒的跟踪需求。

Abstract: Intelligent video surveillance is a key technology in public security, traffic management and community security, where real-time moving target tracking serves as a core component. The traditional MeanShift algorithm has the advantages of simple calculation, strong real-time performance and partial occlusion resistance. However, in complex surveillance scenarios, it suffers from tracking drift, target loss and error accumulation under fast target motion, scale variation, background interference and illumination fluctuation, resulting in insufficient tracking robustness. Therefore, an improved MeanShift tracking algorithm with feature matching-assisted initialization is proposed in this paper. The algorithm quickly locates the target motion region via feature matching, obtains the target centroid through morphological processing to dynamically generate the iterative initial position, and realizes precise target tracking combined with weighted kernel histogram modeling and Bhattacharyya similarity measurement. Experimental results show that the improved algorithm effectively suppresses error accumulation and significantly enhances the tracking stability of fast-moving targets with stable single-frame time, meeting the real-time and robust tracking requirements of intelligent video surveillance.