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Target tracking method in multi-camera scene based on SIFT (Scale Invariant Feature Transform)

A multi-camera, target tracking technology, applied in the field of target tracking, can solve problems such as unintuitive and unproposed solutions

Pending Publication Date: 2021-07-23
HOHAI UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these methods can achieve target tracking across cameras, they are not intuitive and require multiple screens to be displayed together
Moreover, when multiple targets appear in different camera images, only cross-camera tracking of overlapping images can be achieved, but how to assign the target IDs of non-overlapping images does not propose a suitable solution

Method used

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  • Target tracking method in multi-camera scene based on SIFT (Scale Invariant Feature Transform)
  • Target tracking method in multi-camera scene based on SIFT (Scale Invariant Feature Transform)
  • Target tracking method in multi-camera scene based on SIFT (Scale Invariant Feature Transform)

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Embodiment Construction

[0032] refer to Figure 1 to Figure 4 , the present invention provides a SIFT-based target tracking method in a multi-camera scene. The present invention utilizes YOLO-V5s in combination with an improved DeepSort algorithm and an image splicing algorithm to realize the splicing of different camera images. Finally, the spliced ​​video to achieve multi-target tracking. In terms of data sets, self-made smart car data sets and self-made vehicle re-identification data sets containing smart cars are used. Specific steps are as follows:

[0033] S1: Take photos of several smart cars used in this experiment, and mark each photo to make a self-made smart car data set;

[0034] S2: Summarize the self-made smart car data set and the VOC2012 data set to obtain the total target detection data set for the training of the YOLO-V5s model in this experiment;

[0035] S3: Take multi-angle pictures of each smart car, extract the part of each photo that contains the smart car, and obtain the s...

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Abstract

The invention discloses a target tracking method in a multi-camera scene based on SIFT, and the method comprises the steps: obtaining a target detection total data set which is formed by photographing pictures containing different types of detection targets; using the target detection total data set to train a target detector YOLO-V5s model; obtaining a target tracking re-identification data set, wherein the target tracking re-identification data set is formed by taking photos containing different types of tracking targets and extracting the part containing the tracking target in each photo; training a target appearance feature extraction network in a DeepSort algorithm by using the target tracking re-identification data set; and acquiring a video shot by splicing multiple cameras by using an SIFT algorithm, and tracking a tracking target in the video by using the trained YOLO-V5s model in combination with the trained DeepSort algorithm. According to the invention, a larger target detection range can be obtained, and the target tracking precision is improved.

Description

technical field [0001] The invention relates to the technical field related to computer vision, in particular to a SIFT-based target tracking method in a multi-camera scene. Background technique [0002] With the development of information technology, the detection and tracking of moving objects based on vision has gradually penetrated into all aspects of people's lives, and its importance has become increasingly prominent. Moving object tracking belongs to the content of video analysis, including motion detection, object classification, object tracking, event detection, etc. Research on video-based target tracking methods is an important branch of computer vision, and vision-based target detection and tracking is an interdisciplinary research topic in many disciplines such as image processing, computer vision, and pattern recognition. It has important theoretical research significance and practical application value in the fields of interaction and autonomous navigation. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/292G06T7/246G06T3/40G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/292G06T7/248G06T3/4038G06N3/08G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30232G06T2200/32G06V10/462G06N3/048G06N3/045G06F18/241
Inventor 卢新彪刘雅童毛克春施宇豪唐紫婷杭帆
Owner HOHAI UNIV
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