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Cloud-assisted multi-view video summarization using CNN and bi-directional LSTM

Citation

Hussain, T and Muhammad, K and Ullah, A and Cao, Z and Baik, SW and de Albuquerque, VHC, Cloud-assisted multi-view video summarization using CNN and bi-directional LSTM, IEEE Transactions on Industrial Informatics pp. 1-10. ISSN 1551-3203 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 IEEE.

DOI: doi:10.1109/TII.2019.2929228

Abstract

The massive amount of video data produced by surveillance networks in industries instigate various challenges in exploring these videos for many applications such as video summarization, analysis, indexing, and retrieval. The task of multi-view video summarization (MVS) is very challenging due to the gigantic size of data, redundancy, overlapping in views, light variations, and inter-view correlations. To address these challenges, various low-level features and clustering based soft computing techniques are proposed that cannot fully exploit MVS. In this article, we achieve MVS by integrating deep neural network based soft computing techniques in a two tier framework. The first online tier performs target appearance based shots segmentation and stores them in a lookup table that is transmitted to cloud for further processing. The second tier extracts deep features from each frame of a sequence in the lookup table and pass them to deep bi-directional long short-term memory (DB-LSTM) to acquire probabilities of informativeness to generate summary. Experimental evaluation on MVS benchmark dataset and industrial surveillance data from YouTube confirms the higher accuracy of our system compared to state-of-the-art MVS methods.

Item Details

Item Type:Refereed Article
Keywords:artificial intelligence, cloud computing, convolutional neural networks, industrial surveillance, multi-view videos, soft computing, video summarization, LSTM
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:133983
Year Published:2019
Web of Science® Times Cited:2
Deposited By:Information and Communication Technology
Deposited On:2019-07-18
Last Modified:2019-08-30
Downloads:5 View Download Statistics

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