Human Action Recognition in Video Using DBLSTM and ResNet

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Mihanpour Akram; Rashti Mohammad Javad; Alavi Seyed Enayatallah

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

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Title

Human Action Recognition in Video Using DBLSTM and ResNet

Author(s)

Mihanpour Akram | Rashti Mohammad Javad | Alavi Seyed Enayatallah

Keywords

Action Recognition

Video Processing

Deep Neural Networks

Convolutional Neural Network

DB-LSTM

Abstract

Human Action Recognition in video is one of the most widely applied topics in the field of image and Video Processing, with many applications in surveillance (security, sports, etc. ), activity detection, video-content-based monitoring, man-machine interaction, and health/disability care. Action Recognition is a complex process that faces several challenges such as occlusion, camera movement, viewpoint move, background clutter, and brightness variation. In this study, we propose a novel human Action Recognition method using Convolutional Neural Networks (CNN) and deep bidirectional LSTM (DB-LSTM) networks, using only raw video frames. First, deep features are extracted from video frames using a pre-trained CNN architecture called ResNet152. The sequential information of the frames is then learned using the DB-LSTM network, where multiple layers are stacked together in both forward and backward passes of DB-LSTM, to increase depth. The evaluation results of the proposed method using PyTorch, compared to the state-of-theart methods, show a considerable increase in the efficiency of Action Recognition on the UCF 101 dataset, reaching 95% recognition accuracy. The choice of the CNN architecture, proper tuning of input parameters, and techniques such as data augmentation contribute to the accuracy boost in this study.

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APA: Copy

Mihanpour, Akram, Rashti, Mohammad Javad, & Alavi, Seyed Enayatallah. (2020). Human Action Recognition in Video Using DBLSTM and ResNet. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949239/en

Vancouver: Copy

Mihanpour Akram, Rashti Mohammad Javad, Alavi Seyed Enayatallah. Human Action Recognition in Video Using DBLSTM and ResNet. 2020. Available from: https://sid.ir/paper/949239/en

IEEE: Copy

Akram Mihanpour, Mohammad Javad Rashti, and Seyed Enayatallah Alavi, “Human Action Recognition in Video Using DBLSTM and ResNet,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949239/en

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