Foreground extraction method for highway video spilled object detection

A technology of foreground extraction and expressway, applied in image data processing, instruments, character and pattern recognition, etc., can solve the problems of large foreground noise, foreground objects blending into background, etc. low return effect

Active Publication Date: 2020-08-25
ZHEJIANG UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0008] Traditional fixed background modeling methods tend to generate a lot of foreground noise due to changes in the open environment, while various foreground extraction

Method used

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  • Foreground extraction method for highway video spilled object detection
  • Foreground extraction method for highway video spilled object detection
  • Foreground extraction method for highway video spilled object detection

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0048] Embodiment 1 A foreground extraction method for freeway video litter detection.

[0049] S1 establishes a Gaussian mixture model for the road surface in the video sequence, and initializes the model parameters.

[0050] The described establishment of Gaussian mixture model refers to the distribution of K Gaussian background models for each pixel point in the image, and the parameters such as weights, mean values, and variances of K models are initialized;

[0051] Set the weight of the first model to 1, the weight of other models to 0, set the mean to the pixel value of the corresponding pixel in the first frame, and set the variance to 9 or 16;

[0052]

[0053] Among them, P represents the background distribution of the pixel; x j，t Represents the pixel vector of the jth pixel in the image at time t; is the weight of the i-th Gaussian distribution of the j-th pixel in the mixed Gaussian model at time t; Indicates the mean value of the i-th Gaussian distributio...

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Abstract

The invention discloses a foreground extraction method for highway video spilled object detection, and the method comprises the following steps: S1, building a Gaussian mixture model for a pavement ina video sequence, and initializing model parameters; S2, obtaining an initial background by using an adaptive learning rate method, and dividing the initial background to obtain an initial backgroundmodel group B; S3, matching video frame pixel points with the background model, judging whether the type of each pixel point belongs to a foreground or a background, and outputting a binarized foreground image; and S4, updating background model parameters for foreground extraction at the next moment according to a matching result, and judging whether the background model needs to be re-divided through a model weight attenuation strategy or not. The foreground extraction method provided by the invention has the characteristics of high real-time performance and strong adaptive capacity to environmental noise, and is high in accuracy and low in false alarm rate in actual detection.

Description

technical field [0001] The invention relates to the technical field of video image processing, in particular to a foreground extraction method for expressway video spill detection. Background technique [0002] As the traffic volume on expressways continues to increase, the number of accidents caused by spilled objects on expressways has increased sharply. Most of these spills are goods that were not tightly tied up on the truck or debris that fell from a car breakdown. These small and hard objects pose a great threat to the safety belt meters of highway drivers and passengers. Therefore, using video technology to detect the spilled objects on the highway in real time and accurately has become a very meaningful and urgent problem in the field of intelligent transportation. Different from objects such as motor vehicles, fences, signs and markings on the expressway, the types of spilled objects are different, and they do not have the general characteristics of graphics and im...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06T7/194
CPCG06T7/194G06T2207/10016G06T2207/30232G06V20/40G06V20/52
Inventor 夏莹杰欧阳聪宇
Owner ZHEJIANG UNIV
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