Imaging Components, Systems, and Processing

Embedded and real-time vehicle detection system for challenging on-road scenes

[+] Author Affiliations
Qin Gu

University of Electronic Science and Technology of China, School of Electrical and Electronic Engineering, Chengdu, China

Auckland University of Technology, School of Engineering, Computer, and Mathematical Sciences, Department of Electrical and Electronic Engineering, Auckland, New Zealand

Jianyu Yang, Lingjiang Kong

University of Electronic Science and Technology of China, School of Electrical and Electronic Engineering, Chengdu, China

Wei Qi Yan, Reinhard Klette

Auckland University of Technology, School of Engineering, Computer, and Mathematical Sciences, Department of Electrical and Electronic Engineering, Auckland, New Zealand

Opt. Eng. 56(6), 063102 (Jun 09, 2017). doi:10.1117/1.OE.56.6.063102
History: Received April 4, 2017; Accepted May 16, 2017
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Abstract.  Vehicle detection is an important topic for advanced driver-assistance systems. This paper proposes an adaptive approach for an embedded system by focusing on monocular vehicle detection in real time, also aiming at being accurate under challenging conditions. Scene classification is accomplished by using a simplified convolution neural network with hypothesis generation by SoftMax regression. The output is consequently taken into account to optimize detection parameters for hypothesis generation and testing. Thus, we offer a sample-reorganization mechanism to improve the performance of vehicle hypothesis verification. A hypothesis leap mechanism is in use to improve the operating efficiency of the on-board system. A practical on-road test is employed to verify vehicle detection (i.e., accuracy) and also the performance of the designed on-board system regarding speed.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Qin Gu ; Jianyu Yang ; Lingjiang Kong ; Wei Qi Yan and Reinhard Klette
"Embedded and real-time vehicle detection system for challenging on-road scenes", Opt. Eng. 56(6), 063102 (Jun 09, 2017). ; http://dx.doi.org/10.1117/1.OE.56.6.063102


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