Abstract
The emerging areas of smart homes and smart cities need efficient approaches to monitor human behavior in order to assist them in their activities of daily living. The rapid growth in technology led to the use of depth information for privacy-preserving video surveillance, thereby making depth-based automatic video surveillance, a major area of research in academic and industrial communities. In this work, a temporal template representation of depth video is used for human action recognition using convolutional neural networks. A new temporal template representation capturing the spatial occupancy of the subject for a given time-period is proposed to recognize human actions. The ConvNet features extracted from these temporal templates that capture the local discriminative features of these actions are used for action detection. The efficacy of the proposed approach is demonstrated on SDUFall dataset.
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Ijjina, E.P. (2020). Human Fall Detection Using Temporal Templates and Convolutional Neural Networks. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_70
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DOI: https://doi.org/10.1007/978-981-15-1480-7_70
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