A Quick Extraction Algorithm Using Window-Scanning for Moving Vehicles

2013 ◽  
Vol 756-759 ◽  
pp. 3879-3883
Author(s):  
Ji Ze Yang ◽  
Tie Sheng Fan
Keyword(s):  
Initial Point ◽  
Traffic Monitoring ◽  
Video Sequences ◽  
Moving Vehicle ◽  
Space Efficiency ◽  
Distribution Probability ◽  
Moving Vehicles ◽  
Extraction Algorithm ◽  
Stationary Location ◽  
Vehicle Movement

Aiming at the particularity of traffic monitoring video sequences and the regularity of vehicle movement, a quick extraction algorithm using window-scanning for moving vehicles in traffic monitoring videos is proposed in this paper. This algorithm uses hypothesis testing to higher order statistics of frame differences to achieve the rough separation of moving vehicles and background. Then obtain the length of the vehicle and extract the vertical coordinates of the initial point of moving vehicle by setting a static window with a stationary location, combining with the velocity of the vehicle and the moving pixel distribution probability in the window. And obtain the width of the vehicle the horizontal coordinates of the initial point of moving vehicle by setting a dynamic window, combining with the distribution probability of moving pixels in the window. Finally this algorithm achieved the quick extraction of vehicles with the window obtained by length and width, combining with the coordinates of the initial point of moving vehicle. Experiments show the feasibility of the algorithm that the time and space efficiency is relatively high.

scholarly journals Hybrid Video Stabilization for Mobile Vehicle Detection on SURF in Aerial Surveillance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Gao Chunxian ◽  
Zeng Zhe ◽  
Liu Hui
Keyword(s):  
Vehicle Detection ◽  
Emergency Evacuation ◽  
Video Sequences ◽  
Video Stabilization ◽  
Camera Motion ◽  
Aerial Surveillance ◽  
Low Contrast ◽  
Moving Vehicle ◽  
Mobile Vehicle ◽  
Moving Vehicles

Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.

FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas

2011 ◽  
Vol 59 (2) ◽  
pp. 137-140 ◽  
Author(s):  
S. Szczepański ◽  
M. Wöjcikowski ◽  
B. Pankiewicz ◽  
M. KŁosowski ◽  
R. Żaglewski
Keyword(s):  
Urban Areas ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Integrated Sensor ◽  
The Road ◽  
Moving Vehicles ◽  
Direction Of Movement ◽  
Account Due ◽  
Selection Of

FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas This paper describes the idea and the implementation of the image detection algorithm, that can be used in integrated sensor networks for environment and traffic monitoring in urban areas. The algorithm is dedicated to the extraction of moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed. The authors, apart from the careful selection of particular steps of the algorithm towards hardware implementation, also proposed novel improvements, resulting in increasing the robustness and the efficiency. A single, stationary, monochrome camera is used, simple shadow and highlight elimination is performed. The occlusions are not taken into account, due to placing the camera at a location high above the road. The algorithm is designed and implemented in pipelined hardware, therefore high frame-rate efficiency has been achieved. The algorithm has been implemented and tested in FPGA and ASIC.

scholarly journals Detection and Classification of Vehicles on Curved Roads

2019 ◽  
Vol 8 (10S) ◽  
pp. 152-156
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Moving Object Detection ◽  
Morphological Operations ◽  
Moving Vehicles ◽  
Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

Moving Vehicle Detection Using Time Domain Statistical Features

2013 ◽  
Vol 471 ◽  
pp. 208-212 ◽  
Author(s):  
M.P. Paulraj ◽  
Hamid Adom Abdul ◽  
Marhainis Othman Siti ◽  
Sundararaj Sathishkumar
Keyword(s):  
Probabilistic Neural Network ◽  
Hearing Impaired ◽  
Statistical Features ◽  
Experimental Protocol ◽  
Moving Vehicle ◽  
Vehicle Type ◽  
Moving Vehicles ◽  
Machine Interface ◽  
Moving Vehicle Detection ◽  
Sound Signature

The Hearing Impaired People (HIP) cannot distinguish the sound from a moving vehicle approaching from their behind. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. If HIPs can successfully get sound information through some machine interface, dangerous situation will be avoided. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. This paper presents, simple statistical features are used to classify the vehicle type and its distance based on sound signature recorded from the moving vehicles. An experimental protocol is designed to record the vehicle sound under different environment conditions and also at different speed of vehicles. Basic statistical features such as the standard deviation, Skewness, Kurtosis and frame energy have been used to extract the features. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation.

Moving Vehicles Detection in Traffic Video Using Modified SXCS-LBP Texture Descriptor

2015 ◽  
Vol 5 (2) ◽  
pp. 14-34
Author(s):  
Arun Kumar H. D. ◽  
Prabhakar C. J.
Keyword(s):  
Background Modeling ◽  
Experimental Result ◽  
Texture Descriptor ◽  
Illumination Variation ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Spatially Extended ◽  
Traffic Video ◽  
Lbp Descriptor ◽  
Moving Vehicle Detection

Background modeling and subtraction based method for moving vehicle's detection in traffic video using a novel texture descriptor called as Modified Spatially eXtended Center Symmetric Local Binary Pattern (Modified SXCS-LBP) descriptor. The XCS-LBP texture descriptor is sensitive to noise because in order to generate binary code, the value of center pixel value is used as the threshold directly, and it does not consider temporal motion information. In order to solve this problem, this paper proposed a novel texture descriptor called as Modified SXCS-LBP descriptor for moving vehicle detection based on background modeling and subtraction. The proposed descriptor is robust against noise, illumination variation, and able to detect slow moving vehicles because it considers both spatial and temporal moving information. The evaluation carried out using precision and recall metric, which are obtained using experiments conducted on two popular datasets such as BMC and CDnet datasets. The experimental result shows that the authors' method outperforms existing texture and non-texture based methods.

scholarly journals A Calculation Method for Vehicle Movement Reconstruction from Videos

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hao Feng ◽  
Weiguo Shi ◽  
Feng Chen ◽  
Young-Ji Byon ◽  
Weiwei Heng ◽  
...  
Keyword(s):  
Data Collection ◽  
High Speed ◽  
Video Sequences ◽  
One Dimensional ◽  
Spatial Consistency ◽  
Active Calibration ◽  
Actual Movement ◽  
Camera Perspective ◽  
Vehicle Movement ◽  
Data Collection Procedure

This paper proposes a new enhanced method based on one-dimensional direct linear transformation for estimating vehicle movement states in video sequences. The proposed method utilizes a contoured structure of target vehicles, and the data collection procedure is found to be relatively stable and effective, providing a better applicability. The movements of vehicles in the video are captured by active calibration regions while the spatial consistency between the vehicle’s driving track and the calibration information are in sync. The vehicle movement states in the verification phase are estimated using the proposed method first, and then the estimated states are compared with the actual movement states recorded in the experimental test. The results show that, in the case of camera perspective of 90 degrees, in all driving states of low speed, high speed, or deceleration, the error between estimated speed and recorded speed is less than 1.5%, the error of accelerations is less than 7%, and the error of distances is less than 2%; similarly, in the case of camera perspective of 30 degrees, the errors of speeds, distances, and accelerations are less than 4%, 5%, and 10%, respectively. It is found that the proposed method is superior to other existing methods.

scholarly journals Stopping Accidents before They Happen: Perceiving Lane-Level Moving Vehicle Danger Regions to Warn Surrounding Drivers and Pedestrians

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Chi Guo ◽  
Guangyi Cao ◽  
Jieru Zeng ◽  
Jinsong Cui ◽  
Rong Peng
Keyword(s):  
Real Time ◽  
Large Scale ◽  
The Internet ◽  
Active Safety ◽  
Internet Of Vehicles ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Traditional Service ◽  
New Type ◽  
New System

Perceiving the location of dangerous moving vehicles and broadcasting this information to vehicles nearby are essential to achieve active safety in the Internet of Vehicles (IOV). To address this issue, we implement a real-time high-precision lane-level danger region service for moving vehicles. A traditional service depends on static geofencing and fails to deal with dynamic vehicles. To overcome this defect, we devised a new type of IOV service that manages to track dangerous moving vehicles in real time and recognize their danger regions quickly and accurately. Next, we designed algorithms to distinguish the vehicles in danger regions and broadcast the information to these vehicles. Our system can simultaneously manipulate a mass of danger regions for various dangerous vehicles and broadcast this information to surrounding vehicles at a large scale. This new system was tested in Shanghai, Guangzhou, Wuhan, and other cities; the data analysis is presented in this paper as well.

scholarly journals A Lane Extraction Algorithm Based on Fuzzy Set

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yuxiu Bai ◽  
Huanhuan Zheng ◽  
Jian Zhou ◽  
Dongmei Zhou
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Fuzzy Set ◽  
Logistic Regression Model ◽  
Regional Growth ◽  
Membership Degree ◽  
The Road ◽  
Moving Vehicles ◽  
Extraction Algorithm ◽  
Background Area

Lanes are difficult to be extracted completely. A lane extraction algorithm is proposed according to vehicle driving rules. Vehicles are moving constantly, so the foreground area and background area cannot be defined effectively in the image. Therefore, based on the theory of fuzzy set, multidimensional degree is used to judge the membership degree of target and foreground in order to extract the moving area accurately. Then, the logistic regression model is established to determine the moving vehicles. Finally, based on the vehicle track, the lane extraction is realized by regional growth. The results show that the proposed algorithm can extract the road effectively.

scholarly journals Moving Load Identification with Long Gauge Fiber Optic Strain Sensing

2021 ◽  
Vol 16 (3) ◽  
pp. 131-158
Author(s):  
Qingqing Zhang ◽  
Wenju Zhao ◽  
Jian Zhang
Keyword(s):  
Fiber Optic ◽  
Moving Load ◽  
Field Testing ◽  
Maximum Strain ◽  
Load Identification ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Structural Responses ◽  
Vehicle Loads ◽  
Moving Load Identification

Moving load identification has been researched with regard to the analysis of structural responses, taking into consideration that the structural responses would be affected by the axle parameters, which in its turn would complicate obtaining the values of moving vehicle loads. In this research, a method that identifies the loads of moving vehicles using the modified maximum strain value considering the long-gauge fiber optic strain responses is proposed. The method is based on the assumption that the modified maximum strain value caused only by the axle loads may be easily used to identify the load of moving vehicles by eliminating the influence of these axle parameters from the peak value, which is not limited to a specific type of bridges and can be applied in conditions, where there are multiple moving vehicles on the bridge. Numerical simulations demonstrate that the gross vehicle weights (GVWs) and axle weights are estimated with high accuracy under complex vehicle loads. The effectiveness of the proposed method was verified through field testing of a continuous girder bridge. The identified axle weights and gross vehicle weights are comparable with the static measurements obtained by the static weighing.

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