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Human interaction prediction using deep temporal features

Ke, Q., Bennamoun, M., An, S., Boussaid, F. and Sohel, F. (2016) Human interaction prediction using deep temporal features. Lecture Notes in Computer Science, 9914 . pp. 403-414.

Link to Published Version: http://dx.doi.org/10.1007/978-3-319-48881-3_28
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Abstract

Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction.

Publication Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer Verlag
Copyright: 2016 Springer International Publishing Switzerland
Other Information: Book title: Computer Vision – ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II
URI: http://researchrepository.murdoch.edu.au/id/eprint/34929
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