A New Microscopic Approach to Traffic Flow Classification Using a Convolutional Neural Network Object Detector and a Multi-Tracker Algorithm
Traffic congestion is a significant issue in urban areas and can cause adverse effects. In this paper, our proposal categorizes the traffic activity from video through three steps: vehicle monitoring, feature extraction, and classification. The vehicle monitoring step comprises an object detector ba...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2022-04, Vol.23 (4), p.3797-3801 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Ribeiro, Matheus Vieira Lessa Aching Samatelo, Jorge Leonid Cetertich Bazzan, Ana Lucia |
| description | Traffic congestion is a significant issue in urban areas and can cause adverse effects. In this paper, our proposal categorizes the traffic activity from video through three steps: vehicle monitoring, feature extraction, and classification. The vehicle monitoring step comprises an object detector based on a convolutional neural network and multi-object tracker. The feature extraction step uses information related to each detected vehicle, in various points of the road, to represent the traffic condition through three features: density, flow, and velocity. We tested on the UCSD dataset and achieved the best performance with 98.82% of accuracy, which outperformed the state-of-the-art methods. |
| doi_str_mv | 10.1109/TITS.2020.3040594 |
| format | Article |
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| ispartof | IEEE transactions on intelligent transportation systems, 2022-04, Vol.23 (4), p.3797-3801 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Artificial neural networks Classification convolutional neural network Detectors Feature extraction microscopic features Microscopy Monitoring Neural networks object detector and tracker Proposals random forest Support vector machines Task analysis Traffic congestion Traffic flow Traffic flow classification Urban areas Video data |
| title | A New Microscopic Approach to Traffic Flow Classification Using a Convolutional Neural Network Object Detector and a Multi-Tracker Algorithm |
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