学术论文

      基于深度学习的目标跟踪方法研究现状与展望

      Status and prospect of target tracking based on deep learning

      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:
      The inverse synthetic aperture lidar (ISAL) have attracted increasing attention for its merits including small visual tracking which is considered as one of the important research topics in the field of computer vision due to its key role in versatile applications, such as precision guidance, intelligent video surveillance, human -computer interaction, robot navigation and public safety. The basic idea for implementing visual tracking is composed of finding the target object in a video or sequence of images, then determining its exact position in the next successive frames and finally generating the corresponding trajectory of this object. Visual tracking, however, is still a challenging problem in practice while taking into account the abrupt appearance changes of the target objects induced by their non -rigid transformation, the sophisticated lighting variation, the obstruction by the block or similar objects in the background and the camera jitter. Motivated by the successful applications in target detection and recognition in recent years, plenty of deep learning models have been integrated in the visual tracking and better performance over traditional methods was achieved in a series of data evaluations, which opens a new door in the field of visual tracking. In this paper, the overview and progress on visual tracking were summarized. The current challenges and corresponding solving approaches in this field are introduced firstly and in particular, several novel and mainstream visual tracking algorithms based on the deep learning are specially described and analyzed in details, including their basic ideas, advantages and disadvantages and future prospect.
      作者: 罗海波 [1] 许凌云 [1] 惠斌 [2] 常铮 [2]
      作者单位: 中国科学院沈阳自动化研究所,辽宁 沈阳 110016;中国科学院大学,北京 100049;中国科学院光电信息处理重点实验室,辽宁 沈阳 110016;辽宁省图像理解与视觉计算重点实验室,辽宁 沈阳 110016 中国科学院沈阳自动化研究所,辽宁 沈阳 110016;中国科学院光电信息处理重点实验室,辽宁 沈阳 110016;辽宁省图像理解与视觉计算重点实验室,辽宁 沈阳 110016
      刊 名: 红外与激光工程 ISTICEIPKU
      年,卷(期): 2017, 46(5)
      分类号: TP391
      在线出版日期: 2017年7月3日
      基金项目: 总装预研项目