Improvised approach using background subtraction for vehicle detection

Anandhalli, Mallikarjun; Baligar, Vishwanath P.

doi:10.1109/iadcc.2015.7154719

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…Traditional vehicle-detection methods are mainly divided into two types: (1) Static-based methods [1][2][3][4][5][6][7] that use sliding windows or shape feature comparison methods to generate vehicle prediction frames and verify them based on the information in the prediction frames and (2) methods that use the dynamic features of a moving object [8][9][10][11][12] to separate it from the image to obtain the contour of the object. Regarding static-based methods, Mohamed et al [1] proposed a vehicledetection system that uses Haar-like features to extract vehicle shape features and inputs the extracted features into an artificial neural network to realize vehicle classification.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, various morphological operations were used to obtain the outline and bounding box of a moving object, detect moving vehicles, and count the vehicles passing through a designated area. A few researchers have used Gaussian mixture models (GMMs) [9,10] to model the background or adaptive background [11][12][13] with the aim of solving the problem of background subtraction due to background images. Poor foreground segmentation is caused by gradual changes in brightness.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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“…Many researchers have developed different methodologies for object detection in video where many combinations of methods and differences in protocols exist. Background subtraction is used to segment the moving object in the video [3].…”

Section: Introductionmentioning

confidence: 99%

Vehicle detection and tracking for traffic management

Anandhalli¹,

Baligar

Baligar³

et al. 2021

IJ-AI

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<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>

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“…Background subtraction methods calculate the difference image D ( x, y ) between an image I t and its corresponding background model I BG to detect moving objects. Anandhalli and Baligar (2015) proposed an improvised background subtraction model for real-time tracking that solves the problems of shadow detection. Alvar et al (2014) proposed an efficient model for background subtraction that processes only some pixels of each image.…”

Section: Introductionmentioning

confidence: 99%

Real-time detection of moving objects in a video sequence by using data fusion algorithm

Tang

Zhang

et al. 2018

Transactions of the Institute of Measurement and Control

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The moving object detection and tracking technology has been widely deployed in visual surveillance for security, which is, however, an extremely challenge to achieve real-time performance owing to environmental noise, background complexity and illumination variation. This paper proposes a novel data fusion approach to attack this problem, which combines an entropy-based Canny (EC) operator with the local and global optical flow (LGOF) method, namely EC-LGOF. Its operation contains four steps. The EC operator firstly computes the contour of moving objects in a video sequence, and the LGOF method then establishes the motion vector field. Thirdly, the minimum error threshold selection (METS) method is employed to distinguish the moving object from the background. Finally, edge information fuses temporal information concerning the optic flow to label the moving objects. Experiments are conducted and the results are given to show the feasibility and effectiveness of the proposed method.

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Improvised approach using background subtraction for vehicle detection

Cited by 16 publications

References 8 publications

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Vehicle detection and tracking for traffic management

Real-time detection of moving objects in a video sequence by using data fusion algorithm

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