Show simple item record Fernando, H Perera, I de Silva, C 2019-09-04T08:55:40Z 2019-09-04T08:55:40Z
dc.description.abstract Human detection is an attractive field in computer vision. It remains a challenging task in constrained light conditions due to low contrast against the background. Most of the research using visible light cameras have mainly focused on human detection when sufficient light is available. They have used RGB images, which were highly sensitive to changes in visible light conditions. This paper is focused on the infrared video feed, which is less sensitive against visible light illumination changes compared to RGB images. We propose a novel approach to human detection using Deep Convolutional Neural Networks (DCNN) and Kernelized Correlation Filters (KCF). The proposed methodology utilizes the MobileNet, pre-trained on ImageNet for frame-wise human detection and KCF to improve the tracking accuracy with motion dynamics model. In addition to that, preprocessing on raw images has been applied to reduce the noise effects. The proposed methodology performed well in infrared image sequence with 89.71% of human detection accuracy, which is higher than the state of the art methods and real-time applicable in currently available surveillance systems. en_US
dc.subject Deep Convolution Neural Network en_US
dc.subject Histogram equalization en_US
dc.subject Human detection en_US
dc.subject Human tracking en_US
dc.subject Infrared video feed en_US
dc.subject MobileNet en_US
dc.subject Motion dynamic model en_US
dc.title Real-time human detection and tracking in infrared video feed en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.identifier.year 2019 en_US
dc.identifier.conference Moratuwa Engineering Research Conference - MERCon 2019 en_US Moraruwa, Sri Lanka en_US

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