目标跟踪相关综述文章

文章 年份 会议/引用量 IF
Object tracking:A survery 2006 7618
Object Tracking Methods:A Review 2019 554
Multiple object tracking: A literature review 2020 1294
Deep learning for multiple object tracking: a survey 2019 145
Deep Learning for Visual Tracking:A Comprehensive Survey 2021 432 23.60
Deep learning in multi-object detection and tracking: state of the art 2021 305
Deep Learning in Video Multi-Object Tracking: A Survey 2020 807 6

others are coming soon...

  1. 定义:
    It aims to infer the location of an arbitrary target in a video sequence, given only its location in the first frame

  2. 应用:
    traffic monitoring, robotics, autonomous vehicle tracking, medical diagnosis systems, activity recognition, and so on.

  • monitoring of traffic flow and detection of traffic accidents
  • ASIMO humanoid robot
  • path-tracking
  • tracking of ventricular wall and medical instruments control
  • learning activity patterns and human activity recognition(比如说VR)
  1. 挑战:
  • Illumination Variation
  • Background Clutters:the backgroundnear the targethas a similarcolor or textureas the target
  • Low Resolution
  • Scale Variation:the ratio ofbounding boxesof the first frameand the currentframe is out ofthe range
  • Occlusion:the target is partially or fully occluded(被遮挡)
  • Change the target position:During themovement, thetarget may berotated,deformed, and soon.
  • Fast Motion:the motion of theground truth islarge
  1. 方法:
    feature-based, segmentation-based, estimation-based, and learning-based methods
  • generative methodsVS discriminative methods
    都需要求 P ( Y ∣ X ) P(Y\mid X) P(Y∣X),即已知样本x,求其属于类别y的概率。不同的是generative methods需根据公式P(Y∣X)= \\frac{P(X∣Y)P(Y)}{P(X)} 来求,但 ' d i s c r i m i n a t i v e m e t h o d s ' 直接求 来求,但\`discriminative methods\`直接求 来求,但'discriminativemethods'直接求P(Y\\mid X)。(Note that deep learning is belong to discriminative methods)
  1. 方法的评价:
  • Robustness
  • Adaptability
  • Real-time processing of information

more details are provided in this paperObject Tracking Methods:A Review

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