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Exploring Multi-Loss Learning for Multi-View Fine-Grained Vehicle Classification
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DOI：10.1007/s10846-022-01626-z.png)
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摘要
En 中文
Electronic Toll Collection is the combination of multiple components, either technical or operational, organized to optimize system efficiency for specific requirements. Four main components constitute an ETC system: Automated Vehicle Identification (AVI), Automated Vehicle Classification (AVC), Customer Service and Violation Enforcement. The AVI involves the identification of vehicles through the transmission of a unique identifier between an in-vehicle device and a tollbooth or roadside reader. To strengthen the reliability of this process, we propose a computer vision solution applied to AVI with subscription/membership. A camera system is set up to perform vehicle verification by extracting attributes of the vehicle to compare with those found in the membership. We focus on solving vehicle make and model classification by developing a fine-grained vehicle classification system that exploits the multi-camera composition of the system by powering a convolutional neural network with multiple views of the vehicle. We propose a multi-view network that extracts features from multiple views and combines them with late fusion to classify the make and model of the vehicle. We also propose a strategy to give each independent view a contribution to network learning. The presented evaluations show that using information from different views of a vehicle improves the classification performance of the make and model, especially in challenging tolling scenarios.
Keyword:
Fine-grained image classification
Multi-view classification
Vehicle classification
Deep learning
期刊
J
IF:
2.8
论文数:
3.9K
被引数:
6.9K
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