Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition

Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi (2017) Sparse-Filtered Convolutional Neural Networks with Layer-Skipping (SFCNNLS) for intra-class variation of vehicle type recognition. In: Deep Learning for Image Processing Applications. IOS Press, Amsterdam, Netherlands, pp. 194-217. ISBN 978-161499822-8, 978-161499821-1

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DOI/Official URL: https://doi.org/10.3233/978-1-61499-822-8-194

Abstract

Vehicle type recognition has become an important application in Intelligence Transportation Systems (ITSs) to provide a safe and efficient road and transportation infrastructure. There are some challenges in implementing this technology including the complexity of the image that will distract accuracy performance, and how to differentiate intra-class variation of the vehicle, for instance, taxi and car. In this paper, we propose to use a deep learning framework that consists of a Sparse-Filtered Convolutional Neural Network with Layer Skipping (SF-CNNLS) strategy to recognize the vehicle type. We implemented 64 sparse filters in Sparse Filtering to extract discriminative features of the vehicle and 2 hidden layers of CNNLS for further processes. The SF-CNNLS can recognize the different types of vehicles due to the combined advantages of each approach. We have evaluated the SF-CNNLS using various classes of vehicle namely car, taxi, and truck. The implementation of the evaluation is during daylight time with different weather conditions and frontal view of the vehicle. From that evaluation, we able to correctly recognize the classes with almost 91% of average accuracy and successfully recognize the taxi as a different class of car.

Item Type:	Book Chapter
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Classification; Computational intelligence; Convolutional Neural Network with Layer Skipping (CNNLS); Deep Learning; Sparse Filtering; Vehicle type recognition
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	02 Dec 2024 01:25
Last Modified:	02 Dec 2024 01:25
URI:	http://umpir.ump.edu.my/id/eprint/42618
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