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Road background extraction and updating method with fusion of real-time traffic state information

A technology for real-time traffic and status information, applied in closed-circuit television systems, image data processing, instruments, etc., can solve problems such as poor robustness, weak anti-interference ability, and poor adaptability to traffic scenarios, achieving good robustness and adaptability. Good, strong anti-interference ability

Active Publication Date: 2014-10-01
SUN YAT SEN UNIV +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, there are many road background extraction and update algorithms. However, the existing road background extraction and update algorithms generally only consider the common level of background update, such as the situation of fewer vehicles or the spatial level of regional positioning update, and do not Considering the real-time traffic flow conditions, the adaptability of the traffic scene is poor, and it is impossible to avoid the interference caused by the sudden change of traffic flow, too many moving vehicles or the change of light on the background image extraction and update, the anti-interference ability is weak, and the robustness is relatively weak. Difference

Method used

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  • Road background extraction and updating method with fusion of real-time traffic state information
  • Road background extraction and updating method with fusion of real-time traffic state information
  • Road background extraction and updating method with fusion of real-time traffic state information

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Embodiment 1

[0118] This embodiment introduces the process of constructing the road background extraction model in the present invention.

[0119] The process of constructing the road background extraction model includes:

[0120] (1) Prediction and estimation of illumination changes

[0121] Lighting conditions are a non-negligible factor in background updates. Lighting conditions mainly affect the timing and frequency of background updates. Theoretically, the road background does not need to be continuously updated when the lighting conditions do not change significantly. In all-weather video surveillance, the illumination changes mainly include two types: (1) slow daily illumination changes with the passage of time; (2) rapid illumination caused by cloudy weather and fog (including cloud cover, etc.) Variety. The present invention adopts the time series analysis autoregressive estimation model to estimate the illumination change.

[0122] The illumination parameter adopts the I val...

Embodiment 2

[0137] This embodiment introduces the road background adaptive update process model of the present invention.

[0138] The road background adaptive update process includes:

[0139] (1) Selection of initial background

[0140] The selection quality of the initial background will affect the quality of the final background extraction and the convergence speed of the algorithm. Different from the existing image initial video frame selection method, on the basis of the selective frame model, the improved initial background selection method of the present invention is:

[0141] (1) Detect the traffic status level of the current video frame and calculate the confidence parameter value of the video frame, and sort the video frame confidence from high to low by insertion sorting method to form a selected frame sequence.

[0142] (2) From the selected frame sequence, select the first N frames as the required initial background candidate frames, and the N value is obtained by truncati...

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Abstract

The invention discloses a road background extraction and updating method with fusion of real-time traffic state information. The method comprises the steps of A, constructing a selectivity background frame extraction model according to the real-time road traffic operation state and an illumination variation estimation result; B according to the constructed selectivity background frame extraction model, sequentially conducting initial background selection, background updating area detection and background updating processing so as to achieve self-adaptation updating on a road background. According to the method, the selectivity background frame extraction model is constructed according to the real-time road traffic operation state and the illumination variation estimation result, the selectivity background frame extraction model with the fusion of the real-time traffic state information and illumination variation information is built on the basis of a traditional method, the adaptability of the traffic background is good, interference caused by sudden variation of traffic flows or excessive moving vehicles or illumination variation on background images is avoided, the interference resistance is strong, and the robustness is good. The road background extraction and updating method can be widely applied to the field of video traffic monitoring and image processing.

Description

technical field [0001] The invention relates to the fields of video traffic monitoring and image processing, in particular to a method for extracting and updating road backgrounds which integrates real-time traffic state information. Background technique [0002] The extraction and update of the road background is an important technical support for video traffic flow detection technologies such as traffic moving object detection, vehicle target extraction and tracking, and is also a common basic method in image processing. [0003] At present, the core algorithm of the road background extraction and update algorithm includes three aspects, the first is the research of the basic algorithm, the second is the detection of the background update area and the third is the quality of the sample source used. In terms of basic algorithm research and content, it mainly includes two categories: non-model method and model method. The main idea of ​​the non-model method is to gradually ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/40G06K9/46H04N7/18
Inventor 李熙莹佘永业余志罗东华
Owner SUN YAT SEN UNIV
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