Physical Features and Deep Learning-based Appearance Features for Vehicle Classification from Rear View Videos
| Autor: | Ninad Thakoor, Bir Bhanu, Rajkumar Theagarajan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pickup truck
050210 logistics & transportation Computer science business.industry Mechanical Engineering Deep learning 05 social sciences Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Convolutional neural network Ride height Computer Science Applications Visualization 0502 economics and business Automotive Engineering Computer vision Artificial intelligence business Intelligent transportation system |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 21:1096-1108 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2019.2902312 |
| Popis: | Currently, there are many approaches for vehicle classification, but there is no specific study on automated, rear view, and video-based robust vehicle classification. The rear view is important for intelligent transportation systems since not all states in the United States require a frontal license plate on a vehicle. The classification of vehicles, from their rear views, is challenging since vehicles have only subtle appearance differences and there are changing illumination conditions and the presence of moving shadows. In this paper, we present a novel multi-class vehicle classification system that classifies a vehicle into one of four possible classes (sedan, minivan, SUV, and a pickup truck) from its rear view video, using physical and visual features. For a given geometric setup of the camera on highways, we make physical measurements on a vehicle. These measurements include visual rear ground clearance, the height of the vehicle, and the distance between the license plate and the rear bumper. We call these distances as the physical features. The visual features, also called appearance-based features, are extracted using convolutional neural networks from the input images. We achieve a classification accuracy of 93.22% and 91.52% using physical and visual features, respectively. Furthermore, we achieve a higher classification accuracy of 94.81% by fusing both the features together. The results are shown on a dataset consisting of 1831 rear view videos of vehicles and they are compared with various approaches, including deep learning techniques. |
| Databáze: | OpenAIRE |
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