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.
Highlights
Detection of humans and vehicle type recognition have always been popular application domains for computer vision techniques since they can be deployed in a number of scenarios and can be quite effective
Census Transformed Histogram for Encoding Sign Information (CENTRIST) is a visual description technique that was proposed by Wu et al [11] that is used to detect topological sections or scene categories
This paper proposed a feature-based technique for pedestrian detection and vehicle classification in nighttime thermal images
Summary
Detection of humans and vehicle type recognition have always been popular application domains for computer vision techniques since they can be deployed in a number of scenarios and can be quite effective. These range from video forensics where the objective could be to analyze a crime scene to corporate bodies and military establishments, which might employ them for environment surveillance activities. This paper proposes the use of contour-related feature extraction from thermal images which are largely unaffected by widely varying lighting conditions.
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