基于深度学习的目标跟踪方法研究现状与展望

发布时间：2018-08-03 20:55

【摘要】：目标跟踪是计算机视觉领域的重要研究方向之一,在精确制导、智能视频监控、人机交互、机器人导航、公共安全等领域有着重要的作用。目标跟踪的基本问题是在一个视频或图像序列中选择感兴趣的目标,在接下来的连续帧中,找到该目标的准确位置并形成其运动轨迹。目标跟踪是一个颇具挑战性的问题,目标的非刚性变化往往改变了目标的表观模型,同时复杂的光照变化、目标与场景间的遮挡、背景中相似物体的干扰和摄像机的抖动等使目标跟踪任务变得更加困难。近年来,随着深度学习在目标检测和识别等领域中取得巨大的突破,许多学者开始将深度学习模型引入到目标跟踪中,并在一系列数据评测集上取得了优于传统方法的性能,逐渐开启了目标跟踪领域的新篇章。文中将首先阐述目标跟踪问题的难点和基本解决思路;然后根据利用深度学习算法解决目标跟踪问题的不同思路,对当前出现的此类主流算法进行分析,介绍这些算法各自的优缺点及未来的工作方向。
[Abstract]:Target tracking is one of the important research directions in the field of computer vision. It plays an important role in the fields of precision guidance, intelligent video surveillance, human-computer interaction, robot navigation, public safety and so on. The basic problem of target tracking is to select the object of interest in a video or image sequence, find the exact position of the target and form its track in the subsequent successive frames. Target tracking is a challenging problem. The non-rigid changes of the target often change the apparent model of the target, at the same time, the complex illumination changes, the occlusion between the target and the scene. The jamming of similar objects in background and the jitter of camera make target tracking more difficult. In recent years, with the great breakthrough of depth learning in the field of target detection and recognition, many scholars begin to introduce depth learning model into target tracking, and obtain better performance than traditional methods in a series of data evaluation sets. Gradually opened a new chapter in the field of target tracking. In this paper, the difficulties and basic solutions of the target tracking problem are described, and then, according to the different ideas of using the depth learning algorithm to solve the target tracking problem, the current mainstream algorithms are analyzed. The advantages and disadvantages of these algorithms and their future working directions are introduced.
【作者单位】：中国科学院沈阳自动化研究所;中国科学院大学;中国科学院光电信息处理重点实验室;辽宁省图像理解与视觉计算重点实验室;
【基金】：总装预研项目(51301030108)
【分类号】：TP391.41

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