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Multi-target tracking method based on recurrent neural network

A technology of cyclic neural network and multi-target tracking, which is applied in the field of multi-target tracking based on cyclic neural network, can solve problems such as target motion trajectory tracking, achieve the effect of avoiding targeted parameter tuning, simplifying the process, and good tracking effect

Inactive Publication Date: 2016-10-12
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] In order to solve the problems in the prior art that it is necessary to optimize the pertinent parameters of each tracking target, and cannot effectively track the trajectory of the target under complex environmental conditions such as different directions, illumination conditions, and deformations, the present invention provides a method based on Multi-target Tracking Method Based on Recurrent Neural Network

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Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0028] Combine below Figure 1-2 The present invention will be described in detail.

[0029] see figure 1 , a multi-target tracking method based on recurrent neural network, the steps are as follows:

[0030] Step 1: Construct a surveillance video dataset with pedestrian positions marked in each frame. Specifically, the position of each pedestrian target in the monitoring video data is marked in each frame, and different targets are numbered to obtain a monitoring video data set marked with the position of each frame of pedestrians; preferably, it can be publicized through networks such as MOTChanllenge The dataset acquires surveillance video data.

[0031] Step 2: Manually expand the surveillance video data set with pedestrian positions marked in each frame t...

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Abstract

The invention discloses a multi-target tracking method based on a recurrent neural network, comprising the following steps: building a monitoring video data set marked with each frame of pedestrian location; manually expanding the monitoring video data set marked with each frame of pedestrian location to get training set samples; grouping the training set samples to get multiple training groups; building a multi-target tracking network; inputting the training groups in sequences to the multi-target tracking network for training; and inputting video data to be tested to the trained multi-target tracking network and performing forward propagation to get the moving trajectories of multiple targets. According to the invention, a proposed network model is trained in an end-to-end manner using raw data and a large amount of data obtained through artificial expansion, complex tasks such as data association and track estimation are completed under a unified neural network architecture, and the moving trajectories of targets can be tracked effectively under different directions, light conditions, deformations and other complex environments.

Description

technical field [0001] The invention relates to the technical fields of computer vision and machine learning, in particular to a multi-target tracking method based on a cyclic neural network. Background technique [0002] Intelligent monitoring system is a key development direction of today's monitoring industry. It mainly relies on technologies such as computer vision and machine learning to automatically analyze the pictures captured by the monitoring camera, and judge the state of the crowd by tracking the movement trajectories of pedestrians. , Pedestrian flow, etc., can warn the occurrence of emergency events in advance, and provide sufficient response time for the management department. [0003] Deep learning was officially proposed in 2006. It is a popular field in machine learning in recent years. It originated from multi-layer artificial neural networks and has been successfully applied in computer vision, natural language processing and intelligent search. Among t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/42
Inventor 李鸿升范峻铭周辉胡欢曹滨
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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