NDLI: Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images

Content Provider	IEEE Xplore Digital Library
Author	Irhebhude, M.E. Ali, M.A. Edirisinghe, E.A.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK (Irhebhude, M.E.; Ali, M.A.; Edirisinghe, E.A.)
Abstract	This paper proposes a feature-based technique to detect pedestrians and recognize vehicles within thermal images that have been captured during nighttime. The proposed technique applies the support vector machine (SVM) classifier on CENsus Transformed histogRam Oriented Gradient (CENTROG) features in order to classify and detect humans and/or vehicles. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable for nighttime image capturing and subsequent analysis. Since contour is the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better detection and classification accuracy for both pedestrian and detection and vehicle type recognition.
Starting Page	407
Ending Page	412
File Size	247880
Page Count	6
File Format	PDF
e-ISBN	9781467382007
DOI	10.1109/ICCP.2015.7312693
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-03
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Accuracy Computed tomography Image edge detection Feature extraction Vehicles
Content Type	Text
Resource Type	Article

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