Action Change Detection in Video Based on HOG

Fakhredanesh, M.; Roostaie, S.

doi:10.22061/jecei.2020.6949.351

Document Type : Original Research Paper

Authors

Faculty of Electrical and Computer, Malek Ashtar University of Technology, Tehran, Iran

https://doi.org/10.22061/jecei.2020.6949.351

Abstract

Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition problem. Many of these frameworks suppose that each video sequence includes only one action class. Therefore, we need to break down a video sequence into sub-sequences, each containing only a single action class.
Methods: In this paper, we develop an unsupervised action change detection method to detect the time of actions change, without classifying the actions. In this method, a silhouette-based framework will be used for action representation. This representation uses xt patterns. The xt pattern is a selected frame of xty volume. This volume is achieved by rotating the traditional space-time volume and displacing its axes. In xty volume, each frame consists of two axes (x) and time (t), and y value specifies the frame number.
Results: To test the performance of the proposed method, we created 105 artificial videos using the Weizmann dataset, as well as time-continuous camera-captured video. The experiments have been conducted on this dataset. The precision of the proposed method was 98.13% and the recall was 100%.
Conclusion: The proposed unsupervised approach can detect action changes with a high precision. Therefore, it can be useful in combination with an action recognition method for designing an integrated action recognition system.

Keywords

20.1001.1.23223952.2020.8.1.12.1

Main Subjects

Computer Vision

Open Access

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Publisher

Shahid Rajaee Teacher Training University

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Action Change Detection in Video Based on HOG

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Volume 8, Issue 1
January 2020
Pages 135-144

Action Change Detection in Video Based on HOG

References

References

Send comment about this article

Volume 8, Issue 1January 2020Pages 135-144

Volume 8, Issue 1
January 2020
Pages 135-144