Vehicle detection and classification system based on virtual detection zone

Seenouvong, Nilakorn; Watchareeruetai, Ukrit; Nuthong, Chaiwat; Khongsomboon, Khamphong; Ohnishi, Noboru

doi:10.1109/jcsse.2016.7748886

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…Second, the Montevideo Audio and Video Dataset (MAVD), which contains data on different levels of traffic activity and social use characteristics in Montevideo city, Uruguay, was used as the other traffic data set [30]. Finally, GARM Road-Traffic Monitoring (GRAM-RTM) data set [21] has four categories (i.e., cars, trucks, vans, and big-trucks). e total number of different objects in each sequence is 256 for M-30, 235 for M-30-HD, and 237 for Urban 1.…”

Section: Resultsmentioning

confidence: 99%

“…Using the MAVD and GRAM-RTM Data Sets. MAVD traffic data set [30] and GARM Road-Traffic Monitoring (GRAM-RTM) data set [21] were used for evaluating the vehicle counting performance of the proposed method. e videos were recorded with a GoPro Hero 3 camera at a frame rate of 30 fps and a resolution of 1920 × 1080 px.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…Moreover, a real-time vehicle detection and classification system can perform foreground extraction, vehicle detection, vehicle feature extraction, and vehicle classification [20]. To test the proposed method for vehicle classification, a vehicle-feature-based virtual detection zone and virtual detection line, which are predefined for each frame in a video, are used for vehicle feature computation [21]. Grents et al [22] proposed a video-based system that uses a convolutional neural network to count vehicles, classify vehicles, and determine the vehicle speed.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

Lin

Jeng

Lioa

2021

Mathematical Problems in Engineering

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In recent years, vehicle detection and classification have become essential tasks of intelligent transportation systems, and real-time, accurate vehicle detection from image and video data for traffic monitoring remains challenging. The most noteworthy challenges are real-time system operation to accurately locate and classify vehicles in traffic flows and working around total occlusions that hinder vehicle tracking. For real-time traffic monitoring, we present a traffic monitoring approach that overcomes the abovementioned challenges by employing convolutional neural networks that utilize You Only Look Once (YOLO). A real-time traffic monitoring system has been developed, and it has attracted significant attention from traffic management departments. Digitally processing and analyzing these videos in real time is crucial for extracting reliable data on traffic flow. Therefore, this study presents a real-time traffic monitoring system based on a virtual detection zone, Gaussian mixture model (GMM), and YOLO to increase the vehicle counting and classification efficiency. GMM and a virtual detection zone are used for vehicle counting, and YOLO is used to classify vehicles. Moreover, the distance and time traveled by a vehicle are used to estimate the speed of the vehicle. In this study, the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring data set (GRAM-RTM), and our collection data sets are used to verify the proposed method. Experimental results indicate that the proposed method with YOLOv4 achieved the highest classification accuracy of 98.91% and 99.5% in MAVD and GRAM-RTM data sets, respectively. Moreover, the proposed method with YOLOv4 also achieves the highest classification accuracy of 99.1%, 98.6%, and 98% in daytime, night time, and rainy day, respectively. In addition, the average absolute percentage error of vehicle speed estimation with the proposed method is about 7.6%.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

Lin

Jeng

Lioa

2021

Mathematical Problems in Engineering

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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%

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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“…The test results get 97.22% accuracy for light traffic conditions, 79.63% accuracy for heavy traffic conditions and 100% accuracy for the consistency of average object tracking. In [7], Nilakorn et al used the GMM algorithm for vehicle detection and the k-NN algorithm for vehicle classification. This research uses three classification categories namely small-size (bicycle, motorcycle), mid-size (car, van, small truck), and large-size (big truck, bus).…”

Section: Introductionmentioning

confidence: 99%

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

Jaya¹,

Zainuddin²,

Syarif³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

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This paper describes tracking violations of vehicle tax using the identification of type and number plates on vehicles. The case study of this research is in Sulawesi Selatan (Indonesia). The Gaussian Mixture Model (GMM) algorithm is used to detect vehicle types and the Automatic Number Plate Recognition (ANPR) algorithm for detecting vehicle license numbers. The proposed system uses a digital camera with a camera height of 250 cm and a camera tilt angle of 55 degrees. The proposed system design is the stages of preprocessing, feature extraction, feature selection, model selection, and database. For the introduction of types of vehicles are classified based on predetermined ROI while the introduction of vehicle numbers is trained using k-Nearest Neighbor (KNN). The results of the proposed system accuracy are 91% for vehicle type detection and 70% for vehicle license number detection.

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Vehicle detection and classification system based on virtual detection zone

Cited by 12 publications

References 9 publications

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

A Real-Time Vehicle Counting, Speed Estimation, and Classification System Based on Virtual Detection Zone and YOLO

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

Tracking of Vehicle Tax Violations Using Vehicle Type and Plate Number Identification

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