EyeSoft Fire Detector

1. An added module for Eyesoft analytics. 2. Detects Fire and Smoke. 3. Focuses on a particular area of the area that requires protection. 4. Uses video analytics to detect fire and smoke. 5. Can be used in conjunction with other analytics. 6. Use on or of site with IP cameras

NETWORK SURVEILLANCE MANAGEMENT / NVR SOFTWARE
Advanced IP camera & network surveillance management software with easy to use interface and
quick start setup to help you build and manage scaleable IP camera surveillance systems.
OVERVIEW

MAIN FEATURES

EyeSoft is a network video surveillance

4 / 8 / 16 / 32 / 64 ch Versions

software platform designed for small to

Stack Servers for 1000’s cameras

large scale IP video surveillance systems

Support 400+ IP Camera Models

with powerful features.

JPEG / MJPEG / MPEG4 / H.264

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Hybrid Analogue Card Support
EyeSoft is designed to allow easy
deployment by the installer and simple
operation by the end user.

Multi Screen & Instant Playback
Calendar / Timeline / Tag Search
Matrix Supports 8+ Monitors
Connect to Multiple Remote Sites

Build stable and reliable network IP video
recording
features
business

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

1. Introduction

Fires have a duality such that while they can be useful in cooking and manufacturing; they can also cause loss of lives and destruction of property. With fire and some of its by-products being employed in a lot of useful applications such as cooking, power generation, and manufacturing process, among others, it has the potential also to cause havoc.

According to the Center of Fire Statistics of the International Technical Committee for the Prevention and Extinction of Fire (CTIF), between 70,000 and 80,000 deaths occur annually due to fire outbreaks since the beginning of the 21^st century [1]. In fact, GHℂ 1.74M (0.92M USD) was lost due to fire outbreaks in the first quarter of 2012 in Ghana [2].