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JOURNALS // Program Systems: Theory and Applications // Archive

Program Systems: Theory and Applications, 2017 Volume 8, Issue 4, Pages 327–345 (Mi ps278)

This article is cited in 1 paper

Artificial Intelligence, Intelligent Systems, Neural Networks

Action recognition on video using recurrent neural networks

A. Yu. Buykoa, A. N. Vinogradovb

a PRUDN University of Russia
b Ailamazyan Program Systems Institute of Russian Academy of Sciences

Abstract: In this paper, we consider the application of computer vision and recurrent neural networks to solve the problem of identifying and classifying actions on video. The article describes the approach taken by the authors to analyze video files. Recurrent neural networks uses as a classifier. The classifier takes data in a “bags of words” format that describes low-level actions. The histograms contained in a “bags of words” are represented by sets of video file descriptors. Next algorithms are used to search for descriptors: SIFT, ORB, BRISK, AKAZE. (In Russian).

Key words and phrases: computer vision, descriptors, bags of words, deep learning, recurrent neural networks, long short-term memory networks, video analysis.

Received: 04.12.2017
Accepted: 28.12.2017

DOI: 10.25209/2079-3316-2017-8-4-327-345



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