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Target tracking method based on cost-sensitive structured SVM

A cost-sensitive, target tracking technology, applied to instruments, character and pattern recognition, computer components, etc., can solve problems such as easypair and hardpair imbalance

Pending Publication Date: 2020-07-28
中国人民解放军陆军炮兵防空兵学院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The object of the present invention is to provide a target tracking method that can effectively solve the unbalanced problem of easy pair and hard pair existing in the target tracking based on structured SVM, and improve the accuracy and success rate of tracking

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  • Target tracking method based on cost-sensitive structured SVM
  • Target tracking method based on cost-sensitive structured SVM
  • Target tracking method based on cost-sensitive structured SVM

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Embodiment 1

[0115] The experimental data of this embodiment is the OTB100 benchmark data set released in 2015 [Y.Wu, J.Lim, and M.-H.Yang. Object tracking benchmark [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37 (9):1834–1848.], this benchmark contains 100 videos annotated with 11 challenging attributes, namely illumination variation (IV), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), object out of bounds (OV), background clutter (BC), low resolution (LR). The most intuitive and reliable evaluation method in the benchmark library is OPE (one-pass evaluation), which is to run all the videos from beginning to end. In addition, there are two evaluation indicators: Precision (average center error, with 20 pixels as the threshold) and Success (coincidence rate between the detected position and the real position). This embodiment uses Matlab and (OpenCV) to writ...

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Abstract

The invention discloses a target tracking method based on a cost-sensitive structured SVM. The method comprises the following steps: firstly, establishing a cost-sensitive structured SVM model for target tracking, and converting the cost-sensitive structured SVM model into a dual problem according to a Lagrange multiplier method; then solving the cost-sensitive structured SVM model by adopting a dual coordinate descent principle, and estimating the state of the target; and finally, performing evaluation by adopting a multi-scale target tracking method, and selecting the maximum response as a tracking result. According to the target tracking method based on the structured SVM, the problem that ease pair and hard pair are unbalanced in an existing target tracking method based on the structured SVM is solved, and the accuracy and success rate of a target tracking algorithm based on the structured SVM are improved.

Description

technical field [0001] The invention relates to the technical field of computer vision target tracking, in particular to a target tracking method based on cost-sensitive structured SVM. Background technique [0002] Object tracking is a basic research topic in the field of computer vision and an important technology in video analysis, whose goal is to estimate the state of the object using video data. Target tracking has important application value in civilian fields such as video surveillance, vehicle navigation, human-computer interaction, intelligent transportation, motion analysis, and attitude estimation, as well as in military fields such as visual guidance, target positioning, and fire control. In recent years, although target tracking has made great progress, it still faces many problems such as complex background, target change and fast movement, and it is still a hot issue in the field of computer vision. [0003] At present, researchers in the field of object tra...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/41G06V20/46G06F18/2411Y02T10/40
Inventor 袁广林孙子文夏良秦晓燕李从利陈萍李豪琚长瑞
Owner 中国人民解放军陆军炮兵防空兵学院
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