Vehicle Detection Method under Night Circumstance

In intelligent vehicle detection, vehicle detection at night especially detection in the condition of urban street always remains a problem. This paper proposes an effective vehicle detection algorithm. Firstly it extracts effective vehicle edge by the method of embossment which eliminates light interference. Then we detect the vehicle moving area by frame difference method and calculate the threshold by OTSU algorithm. Finally the noise points are removed by erosion and expansion. This method can better extract the moving objects.

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Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

Contrast Media & Molecular Imaging ◽

10.1155/2021/3666622 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Renzheng Xue ◽

Ming Liu ◽

Xiaokun Yu

Keyword(s):

Target Detection ◽

Difference Method ◽

Moving Objects ◽

Target Location ◽

Mean Shift ◽

Detection Algorithm ◽

Moving Target ◽

Size Change ◽

Detection And Tracking ◽

Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

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A Motion Vehicle Detection Method Based on Self-Adaptive Background Subtraction with Cumulative Inter-Frame Difference

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.890 ◽

2013 ◽

Vol 655-657 ◽

pp. 890-894 ◽

Cited By ~ 1

Author(s):

Hong Zheng ◽

Wen Ju An ◽

Zhen Li

Keyword(s):

Background Subtraction ◽

Moving Objects ◽

Detection Method ◽

Vehicle Detection ◽

Frame Difference ◽

Detection Technology ◽

Binary Object ◽

Object Mask ◽

Inter Frame ◽

Self Adaptive

Against the poor accuracy of the vehicle counters extracted by existing vehicle detection technology, a motion vehicle detection method based on self-adaptive background subtraction with cumulative inter-frame difference is proposed in this paper. Cumulative inter-frame difference is used to subtract binary object mask. According to the binary object mask, in the area of moving objects the pixels of last background are used to modify the current background, otherwise the pixels of current image are used. The result of this operation is the current background. Then the background difference method is used to detect moving vehicles.

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Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i3010.0789s319 ◽

2019 ◽

Vol 8 (9S3) ◽

pp. 56-60

Keyword(s):

Object Detection ◽

Moving Objects ◽

Detection Method ◽

Learning Algorithm ◽

Detection Algorithm ◽

Moving Objects Detection ◽

Canny Edge ◽

Objects Detection ◽

Canny Edge Detection Algorithm ◽

Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

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Adaptive Moving Vehicle Detection Algorithm Based on Hybrid Background Subtraction and Frame Difference

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.930 ◽

2014 ◽

Vol 644-650 ◽

pp. 930-933 ◽

Cited By ~ 3

Author(s):

Yan Li Luo ◽

Han Lin Wan ◽

Li Xia Xue ◽

Qing Bin Gao

Keyword(s):

Background Subtraction ◽

Vehicle Detection ◽

Detection Algorithm ◽

Target Image ◽

Moving Vehicle ◽

Frame Difference ◽

Video Frequency ◽

Background Difference ◽

Moving Vehicle Detection ◽

Initial Target

This paper proposes an adaptive moving vehicle detection algorithm based on hybrid background subtraction and frame difference. The background image of continuous video frequency is reconstructed by calculating the maximun probability grayscale using grey histogram; Moving regions is gained by frame defference, the initial target image is obtained by background difference method,moving regions image and initial target image AND,XOR and OR operations to get the vehicle moving target images. Experimental results show that the algorithm can response timely to the actual scene changes and improve the quality of moving vehicle detection.

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