Abstract
In this paper, deep learning based approach is advocated for automatic recognition of civilian targets in thermal infrared images. High variability of target signature and low contrast ratio of targets to background makes the task of target recognition in infrared images challenging, demanding robust adaptable methods capable of capturing these variations. As opposed to the traditional shallow learning approaches which rely on hand engineered feature extraction, deep learning based approaches use environmental knowledge to learn and extract the features automatically. We present convolutional neural network (CNN) based deep learning framework for automatic recognition of civilian targets in infrared images. The performance evaluation is carried on infrared target clips obtained from ‘CSIR-CSIO moving object thermal infrared imagery dataset’. The task involves four categories classification one category representing the background and three categories of targets -ambassador, auto and pedestrians. The proposed CNN framework provides classification accuracy of 88.15 % with all four categories and 98.24 % with only three target categories.
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Acknowledgements
The work is supported in part by funds of Council of Scientific and Industrial Research (CSIR), India under the project OMEGA PSC0202-2.3.1.
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Akula, A., Singh, A., Ghosh, R., Kumar, S., Sardana, H.K. (2017). Target Recognition in Infrared Imagery Using Convolutional Neural Network. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_3
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DOI: https://doi.org/10.1007/978-981-10-2107-7_3
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