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.

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

2021 ◽  
pp. 257-273
Author(s):  
Arun Kumar H. D.
Keyword(s):  
Vehicle Detection ◽  
Background Modeling ◽  
Experimental Result ◽  
Texture Descriptor ◽  
Illumination Variation ◽  
Moving Vehicle ◽  
Spatially Extended ◽  
Traffic Video ◽  
Lbp Descriptor ◽  
Moving Vehicle Detection

In this chapter, the authors proposed background modeling and subtraction-based methods for moving vehicle detection in traffic video using a novel texture descriptor called 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 chapter proposed a novel texture descriptor called 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 is carried out using precision and recall metric, which is obtained using experiments conducted on popular dataset such as BMC dataset. The experimental result shows that the method outperforms existing 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.

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.

An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

2019 ◽  
Vol 39 (2) ◽  
pp. 734-756 ◽  
Author(s):  
Ahilan Appathurai ◽  
Revathi Sundarasekar ◽  
C. Raja ◽  
E. John Alex ◽  
C. Anna Palagan ◽  
...  
Keyword(s):  
Neural Network ◽  
Video Surveillance ◽  
Surveillance System ◽  
Vehicle Detection ◽  
Video Surveillance System ◽  
Moving Vehicle ◽  
Traffic Video ◽  
Moving Vehicle Detection

scholarly journals Detection of Moving Vehicles on Highway using Fuzzy Logic for Smart Surveillance System

2021 ◽  
Vol 12 (1S) ◽  
pp. 419-431
Author(s):  
Ajay Suria, Et. al.
Keyword(s):  
Fuzzy Logic ◽  
Visible Region ◽  
Illumination Variation ◽  
Moving Vehicle ◽  
Medical Security ◽  
Moving Vehicles ◽  
Smart Surveillance ◽  
Paper Work ◽  
Fast Motion ◽  
Smart Surveillance System

The computer vision incorporates a prominent performance in developing models for medical, security and lot more concepts and we need to deal with detecting and tracking the moving objectsuch as moving vehicle or person. Various challenges are available due to environmental issues, illumination variation, or fast motion etc. This paper work has developed a fuzzy logic based method for identifying the moving vehicles though bounding boxas depicted in the green colour. The paper depictsa fuzzy based method to detect and track the object around the location until the specific secured dimension of the device. The extended recognition (EIR) is the proposed method which works on the automatic fuzzy set creation.The EIR consists of the pixels of the surroundings and recognize them with the predefined inputs we have with the EIR algorithm in the repository. This methodology successfully identified and detects vehicles. It can track the instances within that visible region and this is working as a human eye mechanism.

TRAFFIC VIDEO-BASED MOVING VEHICLE DETECTION AND TRACKING IN THE COMPLEX ENVIRONMENT

2009 ◽  
Vol 40 (7) ◽  
pp. 569-588 ◽  
Author(s):  
Tao Gao ◽  
Zheng-Guang Liu ◽  
Shi-Hong Yue ◽  
Jian-Qiang Mei ◽  
Jun Zhang
Keyword(s):  
Vehicle Detection ◽  
Complex Environment ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Traffic Video ◽  
Moving Vehicle Detection

scholarly journals Extended fuzzy background modeling for moving vehicle detection using infrared vision

2011 ◽  
Vol 8 (6) ◽  
pp. 340-345 ◽  
Author(s):  
Yeo Boon Chin ◽  
Lim Way Soong ◽  
Lim Heng Siong ◽  
Wong Wai Kit
Keyword(s):  
Vehicle Detection ◽  
Background Modeling ◽  
Moving Vehicle ◽  
Infrared Vision ◽  
Moving Vehicle Detection

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.

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