scholarly journals Improving Vehicle Detection Accuracy Based on Vehicle Shadow and Superposition Elimination

2015 ◽  
Vol 9 (1) ◽  
pp. 1039-1044 ◽  
Author(s):  
Hongjin Zhu ◽  
Honghui Fan ◽  
Feiyue Ye ◽  
Xiaorong Zhao
Keyword(s):  
Color Space ◽  
Great Influence ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
Surveillance Systems ◽  
Traffic Surveillance ◽  
Hsv Color Space ◽  
Shadow It ◽  
Background Models

Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper presented a method which improves Gaussian mixture model to get adaptive background. The HSV color space was used to eliminate vehicle shadow, it was based on a computational colour space that makes use of our shadow model. Vehicle superposition elimination was discussed based on vehicle contour dilation and erosion method. Experiments were performed to verify that the proposed technique is effective for vehicle detection based traffic surveillance systems. Detection results showed that our approach was robust to widely different background and illuminations.

scholarly journals Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Gang Li ◽  
Huansheng Song ◽  
Shuyu Wang ◽  
Jinliang Kong
Keyword(s):  
Background Subtraction ◽  
Three Dimensional ◽  
Vehicle Detection ◽  
Reconstruction Algorithm ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
License Plate ◽  
Surveillance Systems ◽  
Subtraction Method ◽  
2D Images

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the three-dimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods.

Video Vehicle Detection Method Based on Multiple Color Space Information Fusion

2012 ◽  
Vol 546-547 ◽  
pp. 721-726
Author(s):  
Hong Xiang Shao ◽  
Xiao Ming Duan
Keyword(s):  
Information Fusion ◽  
Traditional Method ◽  
Detection Method ◽  
Color Space ◽  
Vehicle Detection ◽  
Data Accuracy ◽  
Traffic Information ◽  
Detection Accuracy ◽  
Relative Independence ◽  
Hsi Color Space

A detection method which selective fuses the nine detection results of RGB, YCbCr and HSI color space according to the image color space relative independence of each component and complementarities is approached in order to improve vehicle video detection accuracy. The method fuses three different detection results in nine components by the value of H when the value of both S and I are higher and does another three detection results when the value of both S and I are smaller. Experiments show that the method compared to the traditional method using only the detection results of the brightness component improved substantial, reduced empty of the detected vehicle a large extent and increased traffic information data accuracy depending on the detection result.

scholarly journals A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 139299-139312 ◽  
Author(s):  
Zhiyuan Wang ◽  
Jifeng Huang ◽  
Neal N. Xiong ◽  
Xiaoping Zhou ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Smart Cities ◽  
Vehicle Detection ◽  
Surveillance Systems ◽  
Traffic Surveillance ◽  
Detection Scheme

Cast Shadow Removal in a Real-Time Environment

2013 ◽  
Vol 275-277 ◽  
pp. 2548-2554
Author(s):  
Hong Ying Zhang ◽  
Hong Li ◽  
Yi Gang Sun
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Space ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Shadow Removal ◽  
Hsv Color Space ◽  
Cast Shadow ◽  
Novel Algorithm

The cast shadows on the background of the object will distinctly affect the recognition of the foreground objects. Due to the limitation of shadow removal methods utilizing texture, a novel algorithm based on Gaussian Mixture Model (GMM) and HSV color space is proposed. Firstly, moving regions are detected using GMM. Secondly, we make two pre-classifiers accurate and adaptive to the change of shadow by using the features of shadow in RGB and HSV color space. Experimental results show that the proposed method is efficient and robust.

Performance Comparison between RGB and HSV Color Segmentations for Road Signs Detection

2013 ◽  
Vol 393 ◽  
pp. 550-555 ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Nahrul Khair Alang Md Rashid ◽  
Yasir Mohd Mustafah
Keyword(s):  
Color Space ◽  
Performance Comparison ◽  
Detection Accuracy ◽  
Partial Occlusion ◽  
Hsv Color Space ◽  
The Road ◽  
Road Signs ◽  
Illumination Changes ◽  
Rgb Color Space

This paper compares the performance of RGB and HSV color segmentations method in road signs detection. The road signs images are taken under various illumination changes, partial occlusion and rotational changes. The proposed algorithms using both RGB and HSV color space are able to detect the 3 standard types of colored images namely Red, Yellow and Blue. The experiment shows that the HSV color algorithm achieved better detection accuracy compared to RGB color space.

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