Convolutional neural network for vehicle detection in low resolution traffic videos
College
College of Computer Studies
Department/Unit
Computer Technology
Document Type
Conference Proceeding
Source Title
Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016
First Page
277
Last Page
281
Publication Date
7-22-2016
Abstract
Recent works on Convolutional Neural Network (CNN) in object detection and identification show its superior performance over other systems. It is being used on several machine vision tasks such as in face detection, OCR and traffic monitoring. These systems, however, use high resolution images which contain significant pattern information as compared to the typical cameras, such as for traffic monitoring, which are low resolution, thus, suffer low SNR. This work investigates the performance of CNN in detection and classification of vehicles using low quality traffic cameras. Results show an average accuracy equal to 94.72% is achieved by the system. An average of 51.28 ms execution time for a 2GHz CPU and 22.59 ms execution time for NVIDIA Fermi GPU are achieved making the system applicable to be implemented in real-time using 4-input traffic video with 6 fps. © 2016 IEEE.
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Digitial Object Identifier (DOI)
10.1109/TENCONSpring.2016.7519418
Recommended Citation
Bautista, C., Dy, C., Mañalac, M., Orbe, R., & Cordel, M. (2016). Convolutional neural network for vehicle detection in low resolution traffic videos. Proceedings - 2016 IEEE Region 10 Symposium, TENSYMP 2016, 277-281. https://doi.org/10.1109/TENCONSpring.2016.7519418
Disciplines
Computer Sciences
Keywords
Vehicle detectors; Neural networks (Computer science); Traffic monitoring
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