Abstract
The automatic identification of physical activities performed by human beings is referred to as Human Activity Recognition (HAR). It aims to infer the actions of one or more persons from a set of observations captured by sensors, videos or still images. Recognizing human activities from video sequences is a much challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance etc. In this paper, we propose a Convolutional Neural Network (CNN) model named as SV-NET, in order to classify human activities obtained directly from RGB videos. The proposed model has been tested on three benchmark video datasets namely, KTH, UCF11 and HMDB51. The results of the proposed model demonstrate improved performance over some existing deep learning based models.
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Bhattacharya, S., Shaw, V., Singh, P.K., Sarkar, R., Bhattacharjee, D. (2021). SV-NET: A Deep Learning Approach to Video Based Human Activity Recognition. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_2
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