A multi-target tracking method based on time series multi-feature fusion

A multi-feature fusion and multi-target tracking technology, which is applied in the field of multi-target tracking based on time series multi-feature fusion, can solve the problems of mutual occlusion and similar appearance targets between multiple targets.

Active Publication Date: 2019-02-19
WUHAN UNIV
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Problems solved by technology

[0005] At present, the more popular way in the field of multi-target tracking is to rely on the data association algorithm of the detector. This kind of method solves the problems of target initialization, extinction,

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  • A multi-target tracking method based on time series multi-feature fusion
  • A multi-target tracking method based on time series multi-feature fusion
  • A multi-target tracking method based on time series multi-feature fusion

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Embodiment Construction

[0090] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0091] Combine below Figure 1 to Figure 6 Embodiments of the present invention are described. The technical solution of this embodiment is a multi-target tracking method based on time series multi-feature data association, which specifically includes the following steps:

[0092] Step 1: Detect the tracking target in the frame image according to the SSD multi-target detection algorithm, compare the confidence of the tracking target with the SSD detection and the confidence threshold, and count the category of the tracking target and the candidate frame of t...

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Abstract

The invention provides a multi-target tracking method based on time series multi-feature fusion. The method of the invention includes that: the category and the candidate frame of the tracking targetare obtained according to the multi-target detection algorithm; a convolution network and a correlation filter are used to compute a moving prediction center point and screen candidate frames; appearance similarity score is calculated; the motion similarity score is calculated; an interactive feature similarity score is calculated; the selected candidate frame is converted into a tracking frame ofthe current frame image, and the characteristic information of the tracking object is updated ; a moving prediction center point of a tracking target that is not matched to a candidate frame is calculated and the candidate frame is screened; unmatched candidate boxes are associated for existing tracking targets to construct new tracking targets; the overlap degree of each tracking object is calculated by using the intersection-union ratio; the lost targets in the multi-frame images are identified as the lost targets. Compared with the prior art, the invention improves the tracking accuracy.

Description

technical field [0001] The invention relates to the technical fields of computer vision and target tracking, in particular to a multi-target tracking method based on time series multi-feature fusion. Background technique [0002] Target tracking means that in the image sequence, the target that the system is interested in is detected first, the target is accurately positioned, and then the target's motion information is continuously updated during the moving process of the target, so as to achieve continuous tracking of the target. Target tracking can be divided into multi-target tracking and single-target tracking. Single-target tracking only focuses on one target of interest. Its task is to design a motion model or appearance model to solve the influence of factors such as scale transformation, target occlusion, and illumination, and to calibrate the sense frame by frame. The image location corresponding to the object of interest. Compared with single-object tracking, mul...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06K9/62
CPCG06T7/251G06T2207/10016G06T2207/20084G06T2207/20081G06V2201/07G06F18/22
Inventor 田胜陈丽琼邹炼范赐恩杨烨胡雨涵
Owner WUHAN UNIV
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