Open Access

ARTICLE

Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

by Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*, ShuiHua Wang⁶

1 University of Wah, Wah Cantt, 47040, Pakistan
2 National University of Technology (NUTECH), Islamabad, 44000, Pakistan
3 COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
4 Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway
5 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea
6 Department of Mathematics, University of Leicester, Leicester, UK

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2021, 68(2), 2693-2709. https://doi.org/10.32604/cmc.2021.016871

Received 12 January 2021; Accepted 14 February 2021; Issue published 13 April 2021

Abstract

Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information. In phase II, the YOLOv2-squeezeNet model is designed, where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted scores. The proposed method achieved up to 90% correct prediction scores on CASIA-A, CASIA-B, and the CASIA-C benchmark datasets. The proposed method achieved better/improved prediction scores as compared to the recent existing works.

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Citations