一体化多目标跟踪算法研究综述

A Survey on One-Shot Multi-Object Tracking Algorithm

  • 摘要: 视觉多目标跟踪算法(MOT)一直是计算机视觉与视频图像智能分析领域的一个研究热点。近年来,随着深度学习的发展及实际应用需要,越来越多性能优异的一体化多目标跟踪算法被提出,受到研究者的青睐。对近年来广受关注的一体化多目标跟踪算法进行了系统性的综述。从不同的一体化构建思路出发,梳理包括构建出发点、框架设计、方法优缺点、研究趋势等方面的内容,并在权威的MOT Challenge系列数据集上进行性能比较,定量地分析不同的一体化方法的优势和局限性。最后,结合研究现状,提出了一体化多目标跟踪需要重点关注的若干问题及未来展望。

     

    Abstract: Visual multiple object tracking (MOT) has become a hot issue in computer vision and intelligent analysis of video images. In recent years, with the development of deep learning and practical application needs, more and more one-shot MOT algorithms with outstanding performance have been proposed, attracting much attention from researchers. This paper systematically reviews the popular one-shot MOT algorithms. From different construction ideas, the paper summarizes the motivation, framework design, strengths and weaknesses of methods, research trends, etc. Afterwards, we compare the performances of the one-shot MOT algorithms on the public testing set MOT Challenge, and quantitatively analyze the advantages and limitations of different one-shot methods. Finally, some future thoughts, foresight, and key issues that need to be focused on are introduced based on the research status.

     

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