KCF and Kalman-based improved multi-target tracking method, system and device

An improved multi-target tracking technology, which is applied in the field of improved multi-target tracking methods, systems and devices, can solve the problems of large amount of calculation and low tracking accuracy, and achieve high tracking accuracy and good real-time effects

Active Publication Date: 2018-05-18
SHENZHEN UNIV
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AI Technical Summary

Problems solved by technology

[0003] The technical problem mainly solved by the present invention is to provide an improved multi-target tracking method based on KCF and Kalman, a

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  • KCF and Kalman-based improved multi-target tracking method, system and device
  • KCF and Kalman-based improved multi-target tracking method, system and device
  • KCF and Kalman-based improved multi-target tracking method, system and device

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Embodiment Construction

[0024] Hereinafter, exemplary embodiments of the present application will be described with reference to the accompanying drawings. Well-known functions and constructions are not described in detail for clarity and conciseness. Terms described below, which are defined in consideration of functions in the present application, may vary according to user and operator's intention or implementation. Therefore, the terms should be defined on the basis of the disclosure throughout the specification.

[0025] see figure 1 , is a schematic flowchart of the first embodiment of the video surveillance method based on video structured data and deep learning in the present invention. The method includes:

[0026] S10: Read the video.

[0027] Optionally, reading the video includes reading real-time video collected by the camera and / or pre-recorded and saved video data. Wherein, the camera for collecting real-time video may be one of a USB camera and a network camera based on rtsp proto...

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Abstract

The invention discloses a KCF and Kalman-based improved multi-target tracking method comprising the following steps: using a GoogLeNet network model to detect targets and extract characteristic vectors of the targets; combining the prediction position of each target on the current frame in a previous frame tracking link with the current frame target observation position, the overlapping rate and the characteristic vector spatial distance, thus building an association matrix, and using a matching algorithm for matching; updating the tracking box of the directly matched tracking link and the corresponding characteristic vector; using a KCF tracker to locally track a target failed in matching; carrying out weighted fusion for the KCF tracking result and a Kalman tracking result, thus obtaining a position, and updating same; predicting the next frame position of each target in the tracking link. The method can combine with the CNN network to extract the characteristic vectors, and uses theKCF local tracking to improve the tracking effect, thus well solving the target blocking and target error detection problems; in addition, the invention also provides a multi-target tracking system and device.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an improved multi-target tracking method, system and device based on KCF and Kalman. Background technique [0002] With the development of technology in the field of computer vision and its wide application in the field of monitoring, the tracking and analysis of targets is particularly important. Traditional tracking methods are based on the idea of ​​Kalman filter tracker (kalman), but this method is relatively poor in predicting target position information when the target is partially occluded, thereby reducing the accuracy of monitoring. Therefore, in order to meet the needs of the development of intelligent monitoring technology, a target tracking method with high accuracy and good prediction effect and a multi-target tracking system are needed. Contents of the invention [0003] The technical problem mainly solved by the present invention is to provide an improved multi-ta...

Claims

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Application Information

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IPC IPC(8): G06T7/277G06T7/246G06T7/269
CPCG06T7/248G06T7/269G06T7/277G06T2207/10016
Inventor 谢维信王鑫高志坚
Owner SHENZHEN UNIV
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