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

Irhebhude, Martins E and Ali, Mohammad Athar and Edirsinghe, Eran A (2015) Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images. In: 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 3-5 Sept. 2015, Romania.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Vehicles, Feature extraction, Accuracy, Computed tomography, Histograms, Image edge detection
Subjects: T Technology > T Technology (General) > Management information systems
Divisions: School of Computing
Depositing User: Athar Ali
Date Deposited: 12 Mar 2020 12:13
Last Modified: 12 Mar 2020 12:13

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