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.

Moving Vehicle Detection Scheme Using Edge Information and Background Subtraction in YCbCr Color Space

2014 ◽  
Vol 989-994 ◽  
pp. 2605-2608
Author(s):  
Wan Xia Yu ◽  
Jing Su
Keyword(s):  
Edge Detection ◽  
Background Subtraction ◽  
Detection Method ◽  
Color Space ◽  
Vehicle Detection ◽  
Edge Information ◽  
Detection Scheme ◽  
Moving Vehicle ◽  
Ycbcr Color Space ◽  
Edge Detection Method

In this paper, we propose the spatial domain based vehicle detection scheme. This proposed scheme combines the Sobel edge detection method with background subtraction in YCbCr color space. The scheme detects the vehicle in Y(Luminance), Cb and Cr (chrominance) components of the vehicle image using background subtraction and combines the three images. Edge detection method determines edge information of the luminance component of the vehicle image. The image combined with edge detection and background difference is implemented filling and filtering operation. The robustness of the proposed scheme is analyzed considering different types of vehicle image.

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 Perception advances in outdoor vehicle detection for automatic cruise control

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 765-779 ◽  
Author(s):  
S. Álvarez ◽  
M. Á. Sotelo ◽  
M. Ocaña ◽  
D. F. Llorca ◽  
I. Parra ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Detection System ◽  
Low Cost ◽  
Vehicle Detection ◽  
Basic Feature ◽  
Transportation Systems ◽  
Support Vector ◽  
Cruise Control ◽  
Vehicle To Vehicle ◽  
Time Gap

SUMMARYThis paper describes a vehicle detection system based on support vector machine (SVM) and monocular vision. The final goal is to provide vehicle-to-vehicle time gap for automatic cruise control (ACC) applications in the framework of intelligent transportation systems (ITS). The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic feature of the detected objects are first located in the image using vision and then combined with a SVM-based classifier. An intelligent learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road scenes has been created for learning purposes. The classifier is trained using SVM in order to be able to classify vehicles, including trucks. In addition, the vehicle detection system described in this paper provides early detection of passing cars and assigns lane to target vehicles. In the paper, we present and discuss the results achieved up to date in real traffic conditions.

scholarly journals The One-Stage Detector Algorithm Based on Background Prediction and Group Normalization for Vehicle Detection

2020 ◽  
Vol 10 (17) ◽  
pp. 5883
Author(s):  
Fei Lu ◽  
Fei Xie ◽  
Shibin Shen ◽  
Jiquan Yang ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Network Models ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Detector Performance ◽  
One Stage ◽  
Complex Scenes ◽  
The One

Vehicle detection in intelligent transportation systems (ITS) is a very important and challenging task in traffic monitoring. The difficulty of this task is to accurately locate and classify relatively small vehicles in complex scenes. To solve these problems, this paper proposes a modified one-stage detector based on background prediction and group normalization to realize real-time and accurate detection of traffic vehicles. The one-stage detector firstly adds a module to adjust the width and height of anchors and predict the target background, which avoids the problem of the target vehicle missing detection or wrong detection due to the influence of the complicated environments. Then, group normalization and the loss function based on weight attenuation can improve the one-stage detector performance in the training process. The experimental results on traffic monitoring datasets indicate that the improved one-stage detector is superior to the other neural network models in terms of precision at 95.78%.

scholarly journals Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Keyvan Kasiri ◽  
Mohammad Javad Shafiee ◽  
Francis Li ◽  
Alexander Wong ◽  
Justin Eichel
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Surveillance Systems ◽  
Performance Accuracy ◽  
Sparse Set

With the progress in intelligent transportation systems in smartcities, vision-based vehicle detection is becoming an important issuein the vision-based surveillance systems. With the advent ofthe big data era, deep learning methods have been increasinglyemployed in the detection, classification, and recognition applicationsdue to their performance accuracy, however, there are stillmajor concerns regarding deployment of such methods in embeddedapplications. This paper offers an efficient process leveragingthe idea of evolutionary deep intelligence on a state-of-the-art deepneural network. Using this approach, the deep neural network isevolved towards a highly sparse set of synaptic weights and clusters.Experimental results for the task of vehicle detection demonstratethat the evolved deep neural network can achieve a substantialimprovement in architecture efficiency adapting for GPUacceleratedapplications without significant sacrifices in detectionaccuracy. The architectural efficiency of ~4X-fold and ~2X-folddecrease is obtained in synaptic weights and clusters, respectively,while the accuracy of 92.8% (drop of less than 4% compared to theoriginal network model) is achieved. Detection results and networkefficiency for the vehicular application are promising, and opensthe door to a wider range of applications in deep learning.

scholarly journals A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 725 ◽  
Author(s):  
Fernando Hermosillo-Reynoso ◽  
Deni Torres-Roman ◽  
Jayro Santiago-Paz ◽  
Julio Ramirez-Pacheco
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Moving Objects ◽  
Color Space ◽  
Automatic Detection ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Lane Detection ◽  
Computational Time ◽  
The Road

Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period τ , its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value; (2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

2021 ◽  
Vol 17 (2) ◽  
pp. 46-71
Author(s):  
Manipriya Sankaranarayanan ◽  
Mala C. ◽  
Samson Mathew
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Characteristics ◽  
Traffic Video ◽  
Virtual Loop

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

Vehicle Detection Based on Binocular Stereovision and Edge Extraction

2012 ◽  
Vol 190-191 ◽  
pp. 1090-1093
Author(s):  
Fei Tong ◽  
Lei Xu
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accident ◽  
High Efficiency ◽  
Region Of Interest ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Transportation Systems ◽  
Vehicle Safety ◽  
Edge Extraction ◽  
Binocular Stereovision

Traffic accident has already become a significant social problem. Intelligent Transportation Systems (ITS) is vital to improve the vehicle safety. As an important part of ITS, vehicle detection has been widespread concern. This paper presents a vehicle detection algorithm based on binocular stereovision and edge extraction: analyze spatial coordinate feature belong to object point and detect obstacle points; segment Region of Interest; apply symmetry to detect vehicle. The experimental results indicate that the algorithm possess high efficiency and accuracy.

Sign in / Sign up

Export Citation Format

Share Document