关键词: 视频监控, 目标跟踪, 跟踪学习检测, 核相关滤波器, 视觉背景提取

Abstract: At present, intelligent video surveillance has made a high demand for real-time, accuracy and robustness of video target tracking algorithm, but the existing algorithms cannot fully meet the application requirements. In this paper, a foreground classification algorithm based on Visual Background extractor（ViBe） is proposed to improve the speed of TLD detection target. The tracker in TLD framework is realized by Kernel Correlation Filter（KCF）, which improves the accuracy and robustness of the algorithm. To verify the feasibility of the proposed algorithm, OTB-2013 benchmark for video surveillance using is tested and compared with the other four representative tracking algorithms. The experimental results show that the improved TLD algorithm is superior to the contrast algorithm in the robustness and accuracy, and the processing speed can reach 40 frame/s. Compared with the standard TLD algorithm, the tracking distance is improved by 1.52 times and the success rate is improved by 1.2 times. Compared with the KCF algorithm, the tracking speed is improved by 2.7 times and the success rate is 2.04 times.

Key words: video surveillance, target tracking, tracking-learning-detection, kernelized correlation filter, visual background extractor