Abstract
In this work, we investigate the potential of applying deep learning techniques in the field of Visual Lifelogging (VL), which means use a wearable camera to acquire information of an individual. Visual Lifelogging could be interpreted, as a complete and comprehensive black box of human’s daily activities. This black box will offer great potential to extract accurate and opportune knowledge on how people live their lives. The sensing technology advent that allowing efficient sensing of personal activities, had led to huge collections of data that are available. The ability to process this data had increased as well. This is well seen in the popularity and growing interest given by the scientific community to the hot field of lifelogging. Using features that separate activities are vital for human behavior understanding and characterization. In this paper, we emphasize more particularly on human activity recognition (HAR) captured by a low temporal resolution wearable camera. To achieve this goal, we have used an already trained Deep Convolutional Neural Network (DCNN). The training is done on the large Dataset ImageNet, which contains millions of images and transfer this knowledge to recognize and classify automatically the daily human activities into one of the categorized activities. The numerical results of the proposed approach are very encouraging with an accuracy of 98.78%.
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Aksasse, H., Aksasse, B., Ouanan, M. (2020). Deep Convolutional Neural Networks for Human Activity Classification. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_7
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