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.

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

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.

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