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Symmetry 2018, 10(9), 375; https://doi.org/10.3390/sym10090375

Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks

1,2,* and 1
1
College of Intelligence Science, National University of Defense Technology, Changsha 410073, China
2
Department of Computer Science, University College London, London WC1E 6BT, UK
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 24 August 2018 / Accepted: 27 August 2018 / Published: 1 September 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
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Abstract

Nowadays, video surveillance has become ubiquitous with the quick development of artificial intelligence. Multi-object detection (MOD) is a key step in video surveillance and has been widely studied for a long time. The majority of existing MOD algorithms follow the “divide and conquer” pipeline and utilize popular machine learning techniques to optimize algorithm parameters. However, this pipeline is usually suboptimal since it decomposes the MOD task into several sub-tasks and does not optimize them jointly. In addition, the frequently used supervised learning methods rely on the labeled data which are scarce and expensive to obtain. Thus, we propose an end-to-end Unsupervised Multi-Object Detection framework for video surveillance, where a neural model learns to detect objects from each video frame by minimizing the image reconstruction error. Moreover, we propose a Memory-Based Recurrent Attention Network to ease detection and training. The proposed model was evaluated on both synthetic and real datasets, exhibiting its potential. View Full-Text
Keywords: object detection; unsupervised learning; recurrent network; memory; attention; video surveillance object detection; unsupervised learning; recurrent network; memory; attention; video surveillance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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He, Z.; He, H. Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks. Symmetry 2018, 10, 375.

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