Combining Deep and Handcrafted Image Features for Vehicle Classification in Drone Imagery
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Wang, Yufei
Jo, Jun
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Murshed, M
Paul, M
Asikuzzaman, M
Pickering, M
Natu, A
RoblesKelly, A
You, S
Zheng, L
Rahman, A
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Canberra, AUSTRALIA
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Abstract
Using unmanned aerial vehicles (UAVs) as devices for traffic data collection exhibits many advantages in collecting traffic information. This paper presents an efficient method based on the deep learning and handcrafted features to classify vehicles taken from drone imagery. Experimental results show that compared to classification algorithms based on pre-trained CNN or hand-crafted features, the proposed algorithm exhibits higher accuracy in vehicle recognition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.
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2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
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© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Artificial intelligence