NDLI: Vehicle detection, counting and classification in various conditions

Content Provider	IET Digital Library
Author	Kamkar, Shiva Safabakhsh, Reza
Abstract	Intelligent transportation systems have received a lot of attention in the last decades. Vehicle detection is the key task in this area and vehicle counting and classification are two important applications. In this study, the authors proposed a vehicle detection method which selects vehicles using an active basis model and verifies them according to their reflection symmetry. Then, they count and classify them by extracting two features: vehicle length in the corresponding time-spatial image and the correlation computed from the grey-level co-occurrence matrix of the vehicle image within its bounding box. A random forest is trained to classify vehicles into three categories: small (e.g. car), medium (e.g. van) and large (e.g. bus and truck). The proposed method is evaluated using a dataset including seven video streams which contain common highway challenges such as different lighting conditions, various weather conditions, camera vibration and image blurring. Experimental results show the good performance of the proposed method and its efficiency for use in traffic monitoring systems during the day (in the presence of shadows), night and all seasons of the year.
Starting Page	406
Ending Page	413
Page Count	8
ISSN	1751956X
Volume Number	10
e-ISSN	17519578
Issue Number	Issue 6, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/10/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2015.0157
Journal	IET Intelligent Transport Systems
Publisher Date	2016-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Active Basis Model Algebra Bounding Box Camera Vibration Computer Vision And Image Processing Technique Feature Extraction Grey-level Co-occurrence Matrix Image Blurring Image Classification Image Recognition Image Restoration Intelligent Transport System Lighting Condition Matrix Algebra Object Detection Random Forest Reflection Symmetry Time-spatial Image Traffic Engineering Computing Traffic Monitoring System Vehicle Classification Vehicle Counting Vehicle Detection Method Vehicle Length Video Signal Processing Video Streaming Video Streams Weather Condition
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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