Abstract
In this paper, a new approach of learning features based on convolutional neural networks for pedestrian detection in far infrared images is presented. Unlike traditional recognition systems which use hand-designed features like SIFT or HOG, our convolutional networks architecture learns new features and representations more appropriate to the classification task in infrared images. Another pedestrian detector based on logistic regression is designed and compared to convolutional networks based classifier. Our system built over non-visible range sensor may have an important role in next generation robotics, especially in perception, advanced driver assistant systems (ADAS) and intelligent surveillance systems.
This work was partially supported by the National Natural Science Foundation in China (NSFC) under Grants 61473038.
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Khellal, A., Ma, H., Fei, Q. (2015). Pedestrian Classification and Detection in Far Infrared Images. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_47
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DOI: https://doi.org/10.1007/978-3-319-22879-2_47
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