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

Disciplines

Manufacturing | Mechanical Engineering

Keywords

Detectors; Expert systems (Computer science); Security systems

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