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Multi-target tracking method based on multi-agent deep enhancement learning

A multi-target tracking and enhanced learning technology, applied in the field of multi-target tracking based on multi-agent deep enhanced learning, can solve frequent occlusion and other problems, achieve the effect of ensuring accuracy, realizing real-time tracking, and overcoming the incompleteness of artificial design features

Active Publication Date: 2018-11-27
NANJING QIANHE INTERNET OF THINGS TECH CO LTD
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AI Technical Summary

Problems solved by technology

Due to a large number of influencing factors in video scenes, such as: the appearance and disappearance of objects, frequent occlusion between objects, similar appearance of multiple objects, and background noise, etc., multi-object tracking is still a challenging research topic.

Method used

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  • Multi-target tracking method based on multi-agent deep enhancement learning
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  • Multi-target tracking method based on multi-agent deep enhancement learning

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Embodiment Construction

[0018] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0019] A multi-target tracking method based on multi-agent deep reinforcement learning, comprising the following steps:

[0020] (1) if figure 1 As shown, the YOLO V3 target detector is used to detect multiple targets in each frame of the video to be tested; for the tth frame of image, the output result of the target detector is the set D t , set D t Contains the detection results of multiple targets, the detection results are displayed by the target box, and the detection results of a single target are recorded as d t =(x, y, w, h), (x, y) is the coordinates of the center point of the target frame, w, h are the width and height of the target frame respectively;

[0021] (2) Define the following parameters: each detected target is regarded as an agent, expressed as agent i, i∈I≡{1,...,n}, n is the number of agents; Each frame of image is re...

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Abstract

The invention discloses a multi-target tracking method based on multi-agent deep enhancement learning. The method comprises: detecting multiple targets through a target detector, regarding the detected multiple targets as multiple agents, and then using a deep enhancement learning method to obtain a combined action set of the multiple targets, thereby completing multi-target tracking. The method applies a multi-agent deep enhancement learning technology to the multi-target tracking method for the first time, and can overcome technical disadvantages that artificial design features are not comprehensive and not accurate enough, and can improve calculation speed, realize real-time tracking. The method has high multi-target tracking accuracy rate and precision, and the number of misinformationand missing reports is low. The method is less affected by interference factors in a multi-target tracking scene, and tracking results are more accurate.

Description

technical field [0001] The invention relates to a video target tracking method, in particular to a multi-target tracking method based on multi-agent deep reinforcement learning. Background technique [0002] As a hot issue in the field of computer vision, multi-target tracking based on video has been widely used in many application fields, such as: automatic driving, robot navigation, artificial intelligence, etc. Due to a large number of influencing factors in video scenes, such as: the appearance and disappearance of targets, frequent occlusions between targets, similar appearance of multiple targets, and background noise, etc., multiple target tracking is still a challenging research topic. . Contents of the invention [0003] Purpose of the invention: In order to overcome the influence of a large number of interference factors in the prior art on multi-target tracking, the present invention provides a multi-target tracking method based on multi-agent deep reinforcemen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/20G06N3/04G06N3/08
CPCG06T7/20G06T2207/10016G06T2207/20081G06T2207/20084
Inventor 姜明新季仁东荣康王国达陈寒章
Owner NANJING QIANHE INTERNET OF THINGS TECH CO LTD
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