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Deep Atrous Spatial Features-Based Supervised Foreground Detection Algorithm for Industrial Surveillance Systems

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Abstract
Camera-based surveillance systems largely perform an intrusion detection task for sensitive areas. The task may seem trivial but is quite challenging due to environmental changes and object behaviors such as those due to night-time, sunlight, IR camera, camouflage, and static foreground objects, etc. Convolutional neural network based algorithms have shown promise in dealing with these challenges. However, they are exclusively focused on accuracy. This article proposes an efficient supervised foreground detection (SFDNet) algorithm based on atrous deep spatial features. The features are extracted using atrous convolution kernels to enlarge the field-of-view of a kernel mask, thereby encoding rich context features without increasing the number of parameters. The network further benefits from a residual dense block strategy that mixes the mid and high-level features to retain the foreground information lost in low-resolution high-level features. The extracted features are expanded using a novel
pyramid upsampling network. The feature maps are upsampled using bilinear interpolation and pass through a 3x3 convolutional kernel. The expanded feature maps are concatenated with the corresponding mid and low-level feature maps from an atrous feature extractor to further refine the expanded feature maps. The SFDNet showed better performance than high-ranked foreground detection algorithms on the three standard databases. The testing demo can be found at https://drive.google.com/file/d/1z_zEj9Yp7GZeM2gSIwYKvSzQlxMAiarw/view?usp=sharing.
Author(s)
아지말 샤바즈조강현
Issued Date
2021
Type
Article
Keyword
Change detection algorithmsConvolutionConvolutional neural networks (CNN)Detection algorithmsdual-camera sensorsFeature extractioninfrared (IR) cameraintelligent surveillance systemsKernelStandardsSurveillance
DOI
10.1109/TII.2020.3017078
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9134
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_crossref_primary_10_1109_TII_2020_3017078&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Deep%20Atrous%20Spatial%20Features-Based%20Supervised%20Foreground%20Detection%20Algorithm%20for%20Industrial%20Surveillance%20Systems&offset=0&pcAvailability=true
Publisher
IEEE Transactions on Industrial Informatics
Location
미국
Language
영어
ISSN
1551-3203
Citation Volume
17
Citation Number
7
Citation Start Page
4818
Citation End Page
4826
Appears in Collections:
Engineering > IT Convergence
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