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.

An Algorithm for Moving Vehicle Detection Based on Traffic Video

2013 ◽  
Vol 278-280 ◽  
pp. 1292-1296
Author(s):  
Shi Song Zhu ◽  
Min Gu ◽  
Jing Liu
Keyword(s):  
Symmetric Difference ◽  
Moving Vehicle ◽  
Binary Images ◽  
Detection Algorithms ◽  
Moving Vehicles ◽  
Traffic Video ◽  
Background Difference ◽  
Moving Vehicle Detection ◽  
Difference Images ◽  
Better Than

Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. Experimental results show the algorithm used in this paper can detect the moving vehicles better than traditional moving vehicles detection algorithms.

scholarly journals Moving Vehicle Detection and Recognition Technology based on Artificial Intelligence

2022 ◽  
Vol 16 ◽  
pp. 399-405
Author(s):  
Zengfang Shi ◽  
Meizhou Liu
Keyword(s):  
Artificial Intelligence ◽  
Motion Vector ◽  
Texture Features ◽  
Vehicle Detection ◽  
Target Area ◽  
Moving Vehicle ◽  
Car Detection ◽  
Moving Vehicles ◽  
Moving Vehicle Detection ◽  
Detection And Recognition

The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.

scholarly journals Behavioral Dynamics of Pedestrians Crossing between Two Moving Vehicles

2020 ◽  
Vol 10 (3) ◽  
pp. 859 ◽  
Author(s):  
Soon Ho Kim ◽  
Jong Won Kim ◽  
Hyun-Chae Chung ◽  
Gyoo-Jae Choi ◽  
MooYoung Choi
Keyword(s):  
Traffic Accidents ◽  
Vehicle Speed ◽  
Mathematical Tool ◽  
Vehicle Type ◽  
Behavioral Dynamics ◽  
Moving Vehicles ◽  
Bearing Angle ◽  
Human Participants ◽  
Crossing Point ◽  
Insight Into

This study examines the human behavioral dynamics of pedestrians crossing a street with vehicular traffic. To this end, an experiment was constructed in which human participants cross a road between two moving vehicles in a virtual reality setting. A mathematical model is developed in which the position is given by a simple function. The model is used to extract information on each crossing by performing root-mean-square deviation (RMSD) minimization of the function from the data. By isolating the parameter adjusted to gap features, we find that the subjects primarily changed the timing of the acceleration to adjust to changing gap conditions, rather than walking speed or duration of acceleration. Moreover, this parameter was also adjusted to the vehicle speed and vehicle type, even when the gap size and timing were not changed. The model is found to provide a description of gap affordance via a simple inequality of the fitting parameters. In addition, the model turns out to predict a constant bearing angle with the crossing point, which is also observed in the data. We thus conclude that our model provides a mathematical tool useful for modeling crossing behaviors and probing existing models. It may also provide insight into the source of traffic accidents.

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

2020 ◽  
pp. 1-5
Author(s):  
Xu Chen ◽  
Haigang Sui ◽  
Jian Fang ◽  
Mingting Zhou ◽  
Chen Wu
Keyword(s):  
Vehicle Detection ◽  
Moving Vehicle ◽  
Moving Vehicle Detection
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