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 An Improved Roadside Parking Space Occupancy Detection Method Based on Magnetic Sensors and Wireless Signal Strength

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2348 ◽  
Author(s):  
Liangliang Lou ◽  
Jinyi Zhang ◽  
Yong Xiong ◽  
Yanliang Jin
Keyword(s):  
Detection Method ◽  
Dead Zone ◽  
Sampling Rate ◽  
Vehicle Detection ◽  
Magnetic Sensors ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Parking Space ◽  
Wireless Transceivers ◽  
Occupancy Detection

Smart Parking Management Systems (SPMSs) have become a research hotspot in recent years. Many researchers are focused on vehicle detection technology for SPMS which is based on magnetic sensors. Magnetism-based wireless vehicle detectors (WVDs) integrate low-power wireless communication technology, which improves the convenience of construction and maintenance. However, the magnetic signals are not only susceptible to the adjacent vehicles, but also affected by the magnetic signal dead zone of high-chassis vehicles, resulting in a decrease in vehicle detection accuracy. In order to improve the vehicle detection accuracy of the magnetism-based WVDs, the paper introduces an RF-based vehicle detection method based on the characteristics analysis of received signal strengths (RSSs) generated by the wireless transceivers. Since wireless transceivers consume more energy than magnetic sensors, the proposed RF-based method is only activated to extract the data characteristics of RSSs to further judge the states of vehicles when the data feature of magnetic signals is not sufficient to provide accurate judgment on parking space status. The proposed method was evaluated in an actual roadside parking lot and experimental results show that when the sampling rate of magnetic sensor is 1 Hz, the vehicle detection accuracy is up to 99.62%. Moreover, compared with machine-learning-based vehicle detection method, the experimental results show that our method has achieved a good compromise between detection accuracy and power consumption.

scholarly journals A Novel Vehicle Detection Method Based on the Fusion of Radio Received Signal Strength and Geomagnetism

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 58 ◽  
Author(s):  
Liangliang Lou ◽  
Jinyi Zhang ◽  
Yong Xiong ◽  
Yanliang Jin
Keyword(s):  
Error Detection ◽  
Detection Method ◽  
Signal Strength ◽  
Vehicle Detection ◽  
Received Signal Strength ◽  
Detection Accuracy ◽  
Detection Problem ◽  
Traffic Light ◽  
Radio Stations ◽  
Multiple Detection

A geomagnetic signal blind zone exists between the front and rear axle of high-chassis vehicle such as trucks and buses, which leads to multiple-detection problem in detecting those vehicles running at low speed on roads or error-detection problem in the case of the stopping position of the vehicle is not standard when waiting for the traffic light to change. In order to improve the detection accuracy of any type of vehicle running at any speed, a novel two-sensors data fusion vehicle detection method through combining received signal strength from radio stations with geomagnetism around vehicles is designed and verified in the paper. Experimental results show that the accuracy of our proposed method can reach 95.4% and traditional single magnetism-based detection method was only 83.4% in the detection of high-chassis vehicles.

scholarly journals A Fast Positioning Technology for Compensation Hole of Automobile Brake Master Cylinder

2021 ◽  
Vol 252 ◽  
pp. 01018
Author(s):  
Changfu Zhao ◽  
Hongchang Ding ◽  
Guohua Cao ◽  
Han Hou
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
The Self ◽  
Fine Tuning ◽  
Process Time ◽  
Detection Accuracy ◽  
Positioning Accuracy ◽  
Master Cylinder ◽  
Structural Part ◽  
Detection Speed

The compensation hole of the automobile brake master cylinder is an important structural part for adjusting the reservoir and pressure chamber of the brake master cylinder. Its detection accuracy is strictly controlled. However, because the compensation hole is located on the inner wall of the blind hole, the existing detection method cannot meet the testing needs. Therefore, this paper introduces the SSD model into the detection of the compensation hole of the brake master cylinder, and realizes the rapid positioning of the compensation hole by means of network fine-tuning. The compensation hole positioning detection is carried out on the self-developed automobile brake master cylinder compensation hole detector. The entire detection process time is about 5s, and the positioning accuracy is high. We apply the fine-tuning SSD model to the detection of the compensation hole of automobile brake master cylinder, which replaces the traditional method based on human-computer interaction to determine the position of the compensation hole. It has better detection accuracy and faster detection speed, and lays the foundation for the subsequent detection of the size of the compensation hole.

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.

Background Extraction and Vehicle Detection Method Based on Histogram in YCbCr Color Space

2012 ◽  
Vol 182-183 ◽  
pp. 530-534
Author(s):  
Cheng Jun Jin ◽  
Gui Ran Chang ◽  
Wei Cheng ◽  
Hui Yan Jiang
Keyword(s):  
Intelligent Transportation Systems ◽  
Detection Method ◽  
Color Space ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Background Information ◽  
Ycbcr Color Space ◽  
Background Extraction ◽  
Key Techniques

In computer vision-based Intelligent Transportation Systems (ITS), one of the key techniques is to detect the vehicles accurately. In this paper, we propose a background extraction and vehicle detection method based on histogram in YCbCr color space. By using YCbCr color space, the influence of illumination change and shadows is reduced. To solve the problem with change in background itself, we propose a background update method by using the pixel change count and histogram. Experiment results show that the proposed algorithm can effectively extract and update the background information in complicated urban traffic environment. It also improves the accuracy of vehicle detection.

A Fast Forward Vehicle Detection Method by Multiple Vision Clues Fusion

2014 ◽  
Vol 543-547 ◽  
pp. 2647-2651
Author(s):  
Tai Qi Wu ◽  
Ye Zhang ◽  
Bin Bin Wang ◽  
Jia Heng Yu ◽  
De Wei Zhu
Keyword(s):  
Detection Method ◽  
System Development ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Vehicle Body ◽  
Detection Accuracy ◽  
Collision Warning ◽  
Moving Vehicle ◽  
Forward Collision Warning

With the development of intelligent vehicle technology, vehicle detection based on vision analysis has become an research hotspot in forward collision warning system development. Aiming to solve the existing problems in the current vehicle detection methods, for example, the detection accuracy is sensitive to the variation of illumination and object angle, we propose a forward moving vehicle detection method according to multiple vision clues fusion. Firstly, we locate the rough position using vehicle bottom shadow detection. The shadow is detected using an adaptive threshold image segmentation approach twice. Secondly, the symmetry of vehicle body and the perspective of camera field of view are both referenced to remove the inaccurate location in the first stage. The proposed method has been tested on several videos recorded in real urban conditions. Experimental results show that our method achieves 93.67% average detection accuracy in daytime, and its processing speed is more than 25fps. The proposed method has certain application prospects for improving the vision based forward collision warning system performance.

scholarly journals Robust moving object detection under complex background

2010 ◽  
Vol 7 (1) ◽  
pp. 201-210 ◽  
Author(s):  
Ying Ding ◽  
Li Wen-Hui ◽  
Fan Jing-Tao ◽  
Yang Hua-Min
Keyword(s):  
Object Detection ◽  
Choquet Integral ◽  
Color Space ◽  
Moving Object Detection ◽  
Moving Object ◽  
Detection Accuracy ◽  
Light Illumination ◽  
Illumination Changes ◽  
Hsi Color Space ◽  
Novel Method

We present a novel method to robustly and efficiently detect moving object, even under the complexity background, such as illumination changes, long shadows etc. This work is distinguished by three key contributions. The first is the integration of the Local Binary Pattern texture measure which extends the moving object detection work for light illumination changing. The second is the introduction of HSI color space measure which removes shadows for the background subtraction. The third contribution is a novel fuzzy way using the Choquet integral which improves detection accuracy. The experiment results using several dataset videos show the robustness and effectiveness of the proposed method.

scholarly journals Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2012
Author(s):  
JongBae Kim
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Noise Removal ◽  
Detection Accuracy ◽  
Vehicle Exhaust ◽  
Learning Techniques ◽  
Real Time Detection

This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.

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