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Poster: Advanced Feature Based Deep Learning for Intelligent Human Activity Recognition: An Approach using Scene Context and Composition of Sub Events.

Published: 25 June 2016 Publication History

Abstract

We investigate the problem of automatic action recognition and classification of videos, using deep learning techniques. Deep learning specializes on generating hierarchical features in spatial domain, but extending it to temporal axis, still remains an open problem. In our work, we explore how to optimally provide calculated low level motion features to the network, as the network itself is not able to capture temporal dependencies from raw input frames. We also experiment on the effect of providing static scene context information to the network, in the task of recognizing actions. Furthermore, we focus on how to compose sub events, in order to obtain a higher level semantic meaning for more complex events, by setting up a network of LSTM units on top of the system.

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cover image ACM Conferences
MobiSys '16 Companion: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion
June 2016
172 pages
ISBN:9781450344166
DOI:10.1145/2938559
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2016

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Author Tags

  1. action recognition
  2. deep learning
  3. lstm
  4. rnn

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  • Poster

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  • National research council Sri Lanka

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MobiSys'16
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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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