Research on the Vehicle Detection Technology Based on Improved Burendra Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1733-1736
Author(s):  
Qiang Li ◽  
Dong Shen ◽  
Xiao Kang Wu
Keyword(s):  
Light Intensity ◽  
Real Time ◽  
Difference Method ◽  
Vehicle Detection ◽  
Moving Targets ◽  
Time Performance ◽  
Video Images ◽  
Detection Technology ◽  
Background Difference

To the background establishment in background difference method, a method is proposed to detect moving targets based on the improved Burendra algorithm. This method can accurately detect moving targets in the video images, meanwhile it can respond to the changes of background and light intensity in the scene in time, being with good real-time performance and robustness.

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

2021 ◽  
pp. 1-13
Author(s):  
Andres Bell ◽  
Tomas Mantecon ◽  
Cesar Diaz ◽  
Carlos R. del-Blanco ◽  
Fernando Jaureguizar ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Time Performance

scholarly journals A Phlegm Stagnation Monitoring Based on VDS Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhiguo Gao ◽  
Xin Yu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Moving Objects ◽  
Recognition Rate ◽  
Differential Method ◽  
Video Frame ◽  
Moving Targets ◽  
System A ◽  
Video Images ◽  
Svm Algorithm

In the nonmedical sputum monitoring system, a practical solution for phlegm stagnation care of patients was proposed. Through the camera, the video images of patients’ laryngeal area were obtained in real time. After processing and analysis on these video frame images, the throat movement area was found out. A three-frame differential method was used to detect the throat moving targets. Anomalies were identified according to the information of moving targets and the proposed algorithm. Warning on the abnormal situation can help nursing personnel to deal with sputum blocking problem more effectively. To monitor the patients’ situation in real time, this paper proposed a VDS algorithm, which extracted the speed characteristics of moving objects and combined with the DTW algorithm and SVM algorithm for sequence image classification. Phlegm stagnation symptoms of patients were identified timely for further medical care. In order to evaluate the effectiveness, our method was compared with the DTW, SVM, CTM, and HMM methods. The experimental results showed that this method had a higher recognition rate and was more practical in a nonmedical monitoring system.

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

Complexity ◽  
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.

scholarly journals Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1728 ◽  
Author(s):  
Bo Yang ◽  
Sheng Zhang ◽  
Yan Tian ◽  
Bijun Li
Keyword(s):  
Motion Vector ◽  
Vehicle Detection ◽  
Feature Points ◽  
Spatial Characteristics ◽  
Video Images ◽  
Detection Technology ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Spatial And Temporal Characteristics ◽  
Background Feature

Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos.

A Method for Traffic Collision Detection

2012 ◽  
Vol 220-223 ◽  
pp. 2606-2610
Author(s):  
Dong Yin ◽  
Fan Zhang ◽  
Kun Wang
Keyword(s):  
Prior Knowledge ◽  
Collision Detection ◽  
Difference Method ◽  
Traffic Accident ◽  
Detection Method ◽  
Vehicle Tracking ◽  
Video Images ◽  
Frame Difference ◽  
Vehicle Collision ◽  
Detection Technology

This paper presents a detection method for traffic accident in real-time video images. Firstly, according to improved average background model, frame difference method and edge detection technology are used to detect vehicles. Secondly, vehicle tracking is accomplished by matching the distance, area and histogram of the same vehicle in next frame. Finally, using the concept of collision area and key point as pre-qualification, the situation of vehicle collision will be accurately detected by prior knowledge and histogram information. The experiments results show that our method is effective.

Vehicle Detection Method for Intelligent Vehicle at Night Time Based on Video and Laser Information

2017 ◽  
Vol 32 (04) ◽  
pp. 1850009 ◽  
Author(s):  
Rong-Hui Zhang ◽  
Feng You ◽  
Fang Chen ◽  
Wen-Qiang He
Keyword(s):  
Gabor Filter ◽  
Region Growing ◽  
Vehicle Detection ◽  
Research Field ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Night Time ◽  
Video Images ◽  
Detection Technology ◽  
Svm Algorithm

Front vehicle detection technology is one of the hot spots in the advanced driver assistance system research field. This paper puts forward a method for front vehicles detection based on video-and-laser-information at night. First of all, video images and laser data are pre-processed with the region growing and threshold area expunction algorithm. Then, the features of front vehicles are extracted by use of a Gabor filter based on the uncertainty principle, and the distances to front vehicles are obtained through laser point cloud. Finally, front vehicles are automatically classified during identification with the improved sequential minimal optimization algorithm, which was based on the support vector machine (SVM) algorithm. According to the experiment results, the method proposed by this text is effective and it is reliable to identify vehicles in front of intelligent vehicles at night.

Research on Moving Vehicle Detection and Tracking Algorithm Based on Video Image

2011 ◽  
Vol 467-469 ◽  
pp. 1488-1492 ◽  
Author(s):  
Xin Sha Fu ◽  
Juan Zhu
Keyword(s):  
Difference Method ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Tracking Algorithm ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Computer Vision Technology ◽  
Background Difference ◽  
Moving Vehicle Detection

Based on the computer vision technology and the digital image processing technology, the video moving vehicle detection and tracking algorithm is made to be on research; with the base of each characters of the background difference method and the inter-frame difference method, a revised comprehensive difference method is used, and combined with the special traffic video background, a background updating method revised from Surrender Algorithm is proposed. The moving object tracking algorithm based on matching matrix is explained to focus on the problem of failure of tracking moving objects when each of them are kept out. The application of software demonstrates that the method cited in this paper proves to be right and feasible and meet the need of highway operation monitor.

scholarly journals Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4646 ◽  
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Shixin Song ◽  
Silun Peng ◽  
Da Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Model ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Detection Algorithm ◽  
Single Shot ◽  
Efficient Operation ◽  
Time Performance ◽  
Smart Cars

Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people’s lives and property.

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