标跟踪是指:给出目标在跟踪视频第一帧中的初始状态(如位置,尺寸),自动估计目标物体在后续帧中的状态。 目标跟踪分为单目标跟踪和多目标跟踪。 人眼可以比较轻松的在一段时间内跟住某个特定目标。但是对机器而言,这一任务并不简单,尤其是跟踪过程中会出现目标发生剧烈形变、被其他目标遮挡或出现相似物体干扰等等各种复杂的情况。过去几十年以来,目标跟踪的研究取得了长足的发展,尤其是各种机器学习算法被引入以来,目标跟踪算法呈现百花齐放的态势。2013年以来,深度学习方法开始在目标跟踪领域展露头脚,并逐渐在性能上超越传统方法,取得巨大的突破。

知识荟萃

目标跟踪 (Object Tracking/Visual Tracking) 专知荟萃

入门学习

  1.  运动目标跟踪系列(1-17)

  2. 目标跟踪学习笔记(2-4)

  3. 目标跟踪算法之深度学习方法

  4. 基于深度学习的多目标跟踪算法研究

  5. 从传统方法到深度学习,目标跟踪方法的发展概述

  6. 目标跟踪算法 Visual Tracking Algorithm Introduction.

  7. Online Object Tracking: A Benchmark 论文笔记 和 翻译 - [http://blog.csdn.net/shanglianlm/article/details/47376323], [http://blog.csdn.net/roamer_nuptgczx/article/details/51379191]

  8. 计算机视觉中,目前有哪些经典的目标跟踪算法?

进阶文章

NIPS2013

  • DLT: Naiyan Wang and Dit-Yan Yeung. "Learning A Deep Compact Image Representation for Visual Tracking." NIPS (2013).

CVPR2014

ECCV2014

BMVC2014

ICML2015

CVPR2015

ICCV2015

NIPS2016

  • Learnet: Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi. "Learning feed-forward one-shot learners." NIPS (2016).

CVPR2016

ECCV2016

CVPR2017

ICCV2017

PAMI & IJCV & TIP

ArXiv

Benchmark

综述

  1. Visual Tracking: An Experimental Survey. PAMI2014.
    - [http://ieeexplore.ieee.org/document/6671560/], [https://dl.acm.org/citation.cfm?id=2693387]
    - 代码:[http://alov300pp.joomlafree.it/trackers-resource.html]

  2. Online Object Tracking: A Benchmark CVPR2013: Wu Y, Lim J, Yang M H.
    - 网址和代码:[http://cvlab.hanyang.ac.kr/tracker_benchmark/benchmark_v10.html]

  3. A survey of datasets for visual tracking
    - [https://link.springer.com/article/10.1007/s00138-015-0713-y]

  4. Siamese Learning Visual Tracking: A Survey

  5. A survey on multiple object tracking algorithm

Tutorial

  1. Object Tracking
  2. Stanford cs231b Lecture 5: Visual Tracking by Alexandre Alahi Stanford Vision Lab

代码

  1. Hierarchical Convolutional Features for Visual Tracking
  2. Robust Visual Tracking via Convolutional Networks
  3. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
  4. Understanding and Diagnosing Visual Tracking Systems
  5. Visual Tracking with Fully Convolutional Networks
  6. Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
  7. Learning to Track at 100 FPS with Deep Regression Networks
  8. Fully-Convolutional Siamese Networks for Object Tracking
  9. Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
  10. Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network
  11. ECO: Efficient Convolution Operators for Tracking
  12. End-to-end representation learning for Correlation Filter based tracking
  13. Context-Aware Correlation Filter Tracking
  14. CREST: Convolutional Residual Learning for Visual Tracking
  15. 中科院自动化所胡卫明老师组的博士生王强整理的一些benchmark结果以及论文汇总(好多是参考他的,再次感谢)
  16. Benchmark Results of Correlation Filters, 相关滤波这几年在tracking领域应用非常广,效果也很惊人,这是总结的近几年相关的文章,上面进阶文章大多数都有了,但是这个Github链接 把CF 变形的方法都罗列分类的很齐全,建议收藏。
    - [https://github.com/HakaseH/CF_benchmark_results]

领域专家

  1. Ming-Hsuan Yang[http://faculty.ucmerced.edu/mhyang/]
  • Ming-HsuanYang视觉跟踪当之无愧第一人,后面的人基本上都和其有合作关系,他引已上万
  • 代表作: - Robust Visual Tracking via Consistent Low-Rank Sparse Learning - FCT,IJCV2014:Fast Compressive Tracking - RST,PAMI2014:Robust Superpixel Tracking; SPT,ICCV2011, Superpixeltracking - SVD,TIP2014:Learning Structured Visual Dictionary for Object Tracking - ECCV2014: Spatio temporalBackground Subtraction Using Minimum Spanning Tree and Optical Flow - PAMI2011:Robust Object Tracking with Online Multiple Instance Learning - MIT,CVPR2009: Visual tracking with online multiple instance learning - IJCV2008: Incremental Learning for Robust Visual Tracking
  1. Haibin Ling
  2. Huchuan Lu
  3. Hongdong Li
  4. Lei Zhang
  1. Xiaogang Wang
  1. Matej Kristan
  1. João F. Henriques
  2. Martin Danelljan
  1. Kaihua Zhang
  1. Hamed Kiani
  1. Luca Bertinetto
  1. Tianzhu Zhang

datasets

  1. OTB
  2. VOT

初步版本,水平有限,有错误或者不完善的地方,欢迎大家提建议和补充,会一直保持更新,本文为专知内容组原创内容,未经允许不得转载,如需转载请发送邮件至fangquanyi@gmail.com 或 联系微信专知小助手(Rancho_Fang)

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VIP内容

目标分割和目标跟踪是计算机视觉领域的基础研究领域。这两个主题很难处理一些常见的挑战,如遮挡、变形、运动模糊、缩放变化等。前者包含异构对象、交互对象、边缘模糊性和形状复杂性;后者在处理快速运动、不可见和实时处理方面存在困难。结合视频目标分割和跟踪两个问题,可以克服各自的困难,提高视频目标的性能。VOST可广泛应用于视频摘要、高清视频压缩、人机交互、无人驾驶汽车等实际应用中。本综述旨在提供最先进的VOST方法的全面回顾,将这些方法分类为不同的类别,并确定新的趋势。首先,我们将VOST方法大致分为视频对象分割(VOS)和基于分割的对象跟踪(SOT)。根据分割和跟踪机制,将每个类别进一步划分为不同的类型。在此基础上,给出了各时间节点的代表性VOS和SOT方法。其次,对不同方法的技术特点进行了详细的讨论和概述。第三,总结了相关视频数据集的特点,并给出了各种评价指标。最后,我们指出了一系列有趣的工作,并得出了自己的结论。

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Visual object tracking (VOT) is an essential component for many applications, such as autonomous driving or assistive robotics. However, recent works tend to develop accurate systems based on more computationally expensive feature extractors for better instance matching. In contrast, this work addresses the importance of motion prediction in VOT. We use an off-the-shelf object detector to obtain instance bounding boxes. Then, a combination of camera motion decouple and Kalman filter is used for state estimation. Although our baseline system is a straightforward combination of standard methods, we obtain state-of-the-art results. Our method establishes new state-of-the-art performance on VOT (VOT-2016 and VOT-2018). Our proposed method improves the EAO on VOT-2016 from 0.472 of prior art to 0.505, from 0.410 to 0.431 on VOT-2018. To show the generalizability, we also test our method on video object segmentation (VOS: DAVIS-2016 and DAVIS-2017) and observe consistent improvement.

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