基于视觉的目标检测与跟踪综述

基于视觉的目标检测与跟踪是图像处理、计算机视觉、模式识别等众多学科的交叉研究课题，在视频监控、虚拟现实、人机交互、自主导航等领域，具有重要的理论研究意义和实际应用价值.本文对目标检测与跟踪的发展历史、研究现状以及典型方法给出了较为全面的梳理和总结.首先，根据所处理的数据对象的不同，将目标检测分为基于背景建模和基于前景建模的方法，并分别对背景建模与特征表达方法进行了归纳总结.其次，根据跟踪过程有无目标检测的参与，将跟踪方法分为生成式与判别式，对基于统计的表观建模方法进行了归纳总结.然后，对典型算法的优缺点进行了梳理与分析，并给出了其在标准数据集上的性能对比.最后，总结了该领域待解决的难点问题，对其未来的发展趋势进行了展望.

Vision-based Object Detection and Tracking: A Review

Vision-based object detection and tracking is an active research topic in image processing, computer vision, pattern recognition, etc. It finds wide applications in video surveillance, virtual reality, human-computer interaction, autonomous navigation, etc. This survey gives a detail overview of the history, the state-of-the-art, and typical methods in this domain. Firstly, object detection is divided into background-modeling-based methods and foreground-modeling-based methods according to the different data objects processed. Background modeling and feature representation are further summarized respectively. Then, object tracking is divided into generative and discriminative methods according to whether the detection process is involved. Statistical based appearance modeling is presented. Besides, discussions are presented on the advantages and drawbacks of typical algorithms. The performances of different algorithms on benchmark datasets are given. Finally, the outstanding issues are summarized. The future trends of this field are discussed.