Research on target detection and tracking method applied to intelligent monitoring system

Shaona Wang, Yang Liu, Yanan Wang, Linlin Li
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Abstract

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.
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智能监控系统中目标检测与跟踪方法的研究
目标检测与跟踪是智能监控系统中的一项关键技术。然而,设计一种鲁棒、准确、实时的目标检测与跟踪算法仍然是一个难点。本文以目标跟踪为研究重点,重点研究当前主流算法之一,即TLD (Tracking -learning-detection)目标跟踪算法。基于TLD目标跟踪和角点检测理论,提出了一种新的目标跟踪方法。在线构建分类器对目标进行实时跟踪和学习,并在系统中加入目标轮廓检测器作为随机森林检测器的补充,解决了跟踪失败的问题。实验结果表明,该算法能有效克服快速变化或深度遮挡的影响,提高运动目标的跟踪效率。
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