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An Efficient Human Activity Recognition Technique Based on Deep Learning

Article dans une revue avec comité de lecture
Author
KHELALEF, Aziz
238162 Université Hadj Lakhdar Batna 1
ABABSA, Fakhreddine
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
BENOUDJIT, Nabil
238162 Université Hadj Lakhdar Batna 1

URI
http://hdl.handle.net/10985/18281
DOI
10.1134/s1054661819040084
Date
2019
Journal
Распознавание образов и анализ изображе&#1085 / Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications

Abstract

In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition.

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