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Method for tracking multi-target vehicles by adopting MCMC (Markov Chain Monte Carlo) algorithm

A Markov Monte Carlo and vehicle tracking technology, which is applied to road vehicle traffic control systems, calculations, computer components, etc., can solve the problems of relying on moving object recognition and simple applicable environment

Inactive Publication Date: 2011-05-25
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Another important feature of this method is that it can solve the occlusion problem of athletes very well, but it is very dependent on the recognition of moving objects and the applicable environment is relatively simple

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  • Method for tracking multi-target vehicles by adopting MCMC (Markov Chain Monte Carlo) algorithm
  • Method for tracking multi-target vehicles by adopting MCMC (Markov Chain Monte Carlo) algorithm
  • Method for tracking multi-target vehicles by adopting MCMC (Markov Chain Monte Carlo) algorithm

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Embodiment Construction

[0075] The method of the present invention will be further described below in conjunction with the accompanying drawings.

[0076] see figure 1 , figure 2 and image 3 , the specific content of the main part of the multi-target vehicle tracking method based on the Markov Monte Carlo algorithm is as follows:

[0077] 1. Image acquisition

[0078] The camera is used to collect continuous sequence images at an average speed of 24 frames per second for the specific vehicle driving traffic scene. The image size is 320×240 pixels. After digital processing the collected images, the image Q at the tth moment is obtained. t .

[0079] 2. Image color space transformation

[0080] The role of image color space transformation is mainly to reduce noise, especially the impact of sudden changes in illumination, highlight vehicle color, and improve segmentation efficiency in subsequent steps. The main method is to process the image through K-L transformation. The specific implementation...

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Abstract

The invention relates to a method for tracking multi-target vehicles by adopting the MCMC (Markov Chain Monte Carlo) algorithm, comprising the following steps: modeling the multi-target vehicle tracking process by adopting the MCMC algorithm, establishing various preselection states, traversing the preselection states by using the Metropolis-Hastings sampling based simulated annealing algorithm, and selecting the preselection state with the maximum connection probability as the optimal solution. In the method, a limit is set to the transfer equivalence for the first time, and the parameters in the data association are estimated by the experimental data regression fitting, thus the optimal vehicle motion trajectory with maximum posteriori probability significance is obtained, and the vehicles are tracked. The invention solves the problems of frequent shielding splitting of the multi-target vehicles and has the advantages of high multi-target vehicle tracking precision and good real-time performance.

Description

technical field [0001] The invention relates to video segmentation and tracking by using machine vision technology, and more specifically, relates to a multi-target vehicle tracking method combining machine vision with Markov Monte Carlo algorithm. Background technique [0002] With the development of the economy and the improvement of people's living standards, cars have become more and more popular, followed by traffic accidents and vehicle jams, traffic conditions have received more and more attention, and video surveillance systems have emerged As an inevitable trend, the essence of the video surveillance system is to detect the vehicle in the video scene, and then track its trajectory according to a series of characteristics of the vehicle. Target tracking is an important part of the video surveillance system. It describes a space-time relationship of moving objects in the scene. Its essence is to effectively extract useful information of target features from the observ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06T7/20G08G1/017
Inventor 戚其丰刘洋樊利娜
Owner SOUTH CHINA UNIV OF TECH
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