Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 136, Issue -, Pages 105-114Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2019.06.037
Keywords
Video surveillance; Computer vision; Object detection; Real-time systems; Benchmark testing
Ask authors/readers for more resources
Modern surveillance systems combine tens or even hundreds of cameras to ensure a wide area of coverage. With such a large amount of incoming video data, it is difficult for humans to effectively monitor each camera for potential threats. Recent advances in machine learning have brought forth great improvements in computer vision, which forms the key to automating surveillance systems. Previous works on the topic of automated surveillance systems have not been updated to leverage the emerging wave of convolutional neural network (CNN) based algorithms, nor considered the implications of these rapidly evolving frameworks. This work investigates the design of the processing system for a CNN-based automated surveillance system, including off-the-shelf algorithms and hardware components. Performance benchmarks are then used to evaluate several design options that impact processing speed of CNN-based surveillance algorithms. Finally, a set of design considerations are presented for the processing system of a modern CNN-based automated surveillance system. (C) 2019 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available