Abstract
Human activity recognition (HAR) plays a vital role in the fields like security, health analysis, gaming, video streaming, surveillances, etc. Many applications were developed based on HAR using video due to its user-friendly nature and affordable cost. We propose the fusion of convolutional layer and Long-Short-Term Memory deep architecture for HAR. In this model, three convolution layer, two BiLSTM layer is used. Convolution layer extracts local features of the frame data. Extracted features were flattened and feed into BiLSTM layers to process the frame sequence and handles the time dependencies. The performance of the model is evaluated using two public datasets namely UCF101 dataset and HMDB-51 dataset and obtained the average accuracy of 96% and 95%, respectively. Influence of hyperparameter was analysed and tuned parameter is used for implementation. The proposed model provides the better accuracy when compared with other deep learning architectures used for HAR using video.
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Nithyakani, P., Ferni Ukrit, M. (2022). Video Based Human Gait Activity Recognition Using Fusion of Deep Learning Architectures. In: Manogaran, G., Shanthini, A., Vadivu, G. (eds) Proceedings of International Conference on Deep Learning, Computing and Intelligence. Advances in Intelligent Systems and Computing, vol 1396. Springer, Singapore. https://doi.org/10.1007/978-981-16-5652-1_51
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DOI: https://doi.org/10.1007/978-981-16-5652-1_51
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