2022
DOI: 10.3389/fphy.2022.829734
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A Deep Learning Framework for Video-Based Vehicle Counting

Abstract: Traffic surveillance can be used to monitor and collect the traffic condition data of road networks, which plays an important role in a wide range of applications in intelligent transportation systems (ITSs). Accurately and rapidly detecting and counting vehicles in traffic videos is one of the main problems of traffic surveillance. Traditional video-based vehicle detection methods, such as background subtraction, frame difference, and optical flow have some limitations in accuracy or efficiency. In this paper… Show more

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Cited by 7 publications
(2 citation statements)
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“…In [19], a detection model for images that obtain car localization based on background subtraction and a Gaussian mixture model were proposed. Deep learning models, such as convolutional neural networks [7,17,20,21], have been shown to achieve high accuracy in object detection and tracking tasks and have been applied to vehicle counting and monitoring with promising results. Moreover, [22] developed a car recognition and tracking model using a joint probability method with radar and camera data.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…In [19], a detection model for images that obtain car localization based on background subtraction and a Gaussian mixture model were proposed. Deep learning models, such as convolutional neural networks [7,17,20,21], have been shown to achieve high accuracy in object detection and tracking tasks and have been applied to vehicle counting and monitoring with promising results. Moreover, [22] developed a car recognition and tracking model using a joint probability method with radar and camera data.…”
Section: Related Workmentioning
confidence: 99%
“…It is highly accurate but expensive to compute. Therefore, these aspects remain significant topics that must be investigated by researchers [17]. Despite deep learning and other advances, some limitations remain in the use of vehicle-counting and monitoring techniques for traffic management.…”
Section: Introductionmentioning
confidence: 99%