Abstract
We propose a deep learning based technique to classify actions based on Long Short Term Memory (LSTM) networks. The proposed scheme first learns spatial temporal features from the video, using an extension of the Convolutional Neural Networks (CNN) to 3D. A Recurrent Neural Network (RNN) is then trained to classify each sequence considering the temporal evolution of the learned features for each time step. Experimental results on the CMU MoCap, UCF 101, Hollywood 2 dataset show the efficacy of the proposed approach. We extend the proposed framework with an efficient motion feature, to enable handling significant camera motion. The proposed approach outperforms the existing deep models for each dataset.
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Acknowledgements
The authors wish to acknowledge the generous financial support provided by the Science and Engineering Research Board (SERB) of the Department of Science and Technology (DST), the Government of India, for conducting this research work. The financial support was provided through the project numbered ECR/2016/000652.
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Singh, K.K., Mukherjee, S. (2018). Recognizing Human Activities in Videos Using Improved Dense Trajectories over LSTM. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_8
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DOI: https://doi.org/10.1007/978-981-13-0020-2_8
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