Video Target Detection In Underground Mine Based On Background Difference And Edge Detection

In view of the complex environment in the underground mine, the detection of moving targets in surveillance video often had the problems of low detection efficiency and the detection result was greatly affected by noise and shadows. A target extraction method based on fusion background subtraction, inter-frame difference and edge detection was proposed. Firstly, the method used the hybrid gaussian background modeling (GMM) to obtain the accurate background image of the dynamic environment, and the extracted moving targets by using background subtractiont. Then based on the three-frame differential and Canny edge detection, the foreground image and the moving object blob was obtained, which was combined with the background subtraction to eliminate noise and voids, and to avoid missed detection of the moving target. Finally, the shadows in the detection process were removed through pixel ratio and threshold screening, and morphological and connected domain processing were performed. Comparing the improved algorithm with the traditional algorithm, the test results show that the improved algorithm can effectively remove the noise and voids, suppress the shadow, avoid the missed detection target, and have a good detection effect.

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Research on Design of Intelligent Background Differential Model for Training Target Monitoring

Complexity ◽

10.1155/2021/5513788 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Ya Liu ◽

Fusheng Jiang ◽

Yuhui Wang ◽

Lu OuYang ◽

Bo Gao ◽

...

Keyword(s):

Difference Method ◽

Detection Efficiency ◽

Moving Targets ◽

Subsequent Work ◽

Differential Model ◽

Colour Image ◽

Huge Impact ◽

The Difference ◽

Target Monitoring ◽

Background Difference

The detection of moving targets is to detect the change area in a sequence of images and extract the moving targets from the background image. It is the basis. Whether the moving targets can be correctly detected and segmented has a huge impact on the subsequent work. Aiming at the problem of high failure rate in the detection of sports targets under complex backgrounds, this paper proposes a research on the design of an intelligent background differential model for training target monitoring. This paper proposes a background difference method based on RGB colour separation. The colour image is separated into independent RGB three-channel images, and the corresponding channels are subjected to the background difference operation to obtain the foreground image of each channel. In order to retain the difference of each channel, the information of the foreground images of the three channels is fused to obtain a complete foreground image. The feature of the edge detection is not affected by light; the foreground image is corrected. From the experimental results, the ordinary background difference method uses grey value processing, and some parts of the target with different colours but similar grey levels to the background cannot be extracted. However, the method in this paper can better solve the defect of misdetection. At the same time, compared with traditional methods, it also has a higher detection efficiency.

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Research on Vehicle Detection and Shadow Elimination Based on Static Image Sequence

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.2362 ◽

2014 ◽

Vol 602-605 ◽

pp. 2362-2365

Author(s):

Quan Wu Li ◽

Yu Hui Li ◽

Bo Li ◽

Yi Chen

Keyword(s):

Edge Detection ◽

Background Subtraction ◽

Vehicle Detection ◽

Image Sequence ◽

Background Modeling ◽

High Definition ◽

Canny Edge Detection ◽

New Approach ◽

Shadow Elimination ◽

Canny Edge

Focused on static high-definition sequence images captured on the highway bayonet, this paper proposes a new approach for vehicle detection and shadow elimination based on average background modeling, which uses average background model and background subtraction to locate vehicle roughly, eliminates shadow of the vehicle using canny edge detection with dynamic histogram threshold determined by the histogram of the image. Experiments show that this method can locate the position of vehicle quickly and accurately.

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