Surveillance systems integration for real time object identification using weighted bounding single neural network
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management
Volume
2018-January
First Page
1
Last Page
6
Publication Date
7-2-2017
Abstract
In this paper, an implementation of a single neural network that classifies objects using bounding boxes and class probabilities is utilized. This features are combined with a real time surveillance system that can identify multiple targets at the same time. YOLO9000 is a contemporary tool in object detection that can detect and recognize multiple targets under different categories in real-time. The system uses a multi-scale training that varies between sizes and recognizable patterns. Training of the single neural network upon detection and classification of a target varies depending upon the computer specifications. Being a classified as a simple expert system, it may less likely predict false positive results if objects are not pre-trained, but through proper intensive training and more image inputs it can predict objects in a more precise classification. This research is intended to integrate the YOLO9000 67fps concurrent monitor with surveillance hardware. © 2017 IEEE.
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Digitial Object Identifier (DOI)
10.1109/HNICEM.2017.8269461
Recommended Citation
Alimuin, R., Guiron, A., & Dadios, E. P. (2017). Surveillance systems integration for real time object identification using weighted bounding single neural network. HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, 2018-January, 1-6. https://doi.org/10.1109/HNICEM.2017.8269461
Disciplines
Manufacturing | Mechanical Engineering
Keywords
Detectors; Expert systems (Computer science); Security systems
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