An early fire detection algorithm using IP cameras

Wireless Kitchen Fire Prevention System Using Electrochemical Carbon Dioxide Gas Sensor for Smart Home

This paper presents a wireless kitchen fire prevention system that can detect and notify the fire risk caused by gas stoves. The proposed system consists of two modules. The sensor module detects the concentration of carbon dioxide (CO2) near the gas stove and transmits the monitoring results wirelessly. The alarm module, which is placed in other places, receives the data and reminds the user of the stove status. The sensor module uses a cost-efficient electrochemical CO2 sensor and embeds an in situ algorithm that determines the status of the gas stove based on the measured CO2 concentration. For the wireless communication between the modules, on-off keying (OOK) is employed, thereby achieving a longer battery lifetime of the alarm module, low cost, and simple implementation. To increase the lifetime further, a wake-up function based on passive infrared (PIR) sensing is employed in the alarm module. Our system can successfully detect the on state of the stove within 40 s and the off state within 200 s. Thanks to the low-power implementation, in situ algorithm, and wake-up function, the alarm module’s expected battery lifetime is extended to about two months.

AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems

This paper proposes a video-based smoke detection technique for early warning in antifire surveillance systems. The algorithm is developed to detect the smoke behavior in a restricted video surveillance environment, both indoor (e.g., railway carriage, bus wagon, industrial plant, or home/office) or outdoor (e.g., storage area or parking area). The proposed technique exploits a Kalman estimator, color analysis, image segmentation, blob labeling, geometrical features analysis, and M of N decisor, in order to extract an alarm signal within a strict real-time deadline. This new technique requires just a few seconds to detect fire smoke, and it is 15 times faster compared to the requirements of fire-alarm standards for industrial or transport systems, e.g., the EN50155 standard for onboard train fire-alarm systems. Indeed, the EN50155 considers a response time of at least 60 s for onboard systems. The proposed technique has been tested and compared with state-of-art systems using the open access Firesense dataset developed as an output of a European FP7 project, including several fire/smoke indoor and outdoor scenes. There is an improvement of all the detection metrics (recall, accuracy, F1 score, precision, etc.) when comparing Advanced Video SmokE Detection (AdViSED) with other video-based antifire works recently proposed in literature. The proposed technique is flexible in terms of input camera type and frame size and rate and has been implemented on a low-cost embedded platform to develop a distributed antifire system accessible via web browser.

Early Fire Detection on Video Using LBP and Spread Ascending of Smoke

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.

A Video Based Fire Smoke Detection Using Robust AdaBoost

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs)

This article proposes a novel method for detecting forest fires, through the use of a new color index, called the Forest Fire Detection Index (FFDI), developed by the authors. The index is based on methods for vegetation classification and has been adapted to detect the tonalities of flames and smoke; the latter could be included adaptively into the Regions of Interest (RoIs) with the help of a variable factor. Multiple tests have been performed upon database imagery and present promising results: a detection precision of 96.82% has been achieved for image sizes of 960 × 540 pixels at a processing time of 0.0447 seconds. This achievement would lead to a performance of 22 f/s, for smaller images, while up to 54 f/s could be reached by maintaining a similar detection precision. Additional tests have been performed on fires in their early stages, achieving a precision rate of p = 96.62%. The method could be used in real-time in Unmanned Aerial Systems (UASs), with the aim of monitoring a wider area than through fixed surveillance systems. Thus, it would result in more cost-effective outcomes than conventional systems implemented in helicopters or satellites. UASs could also reach inaccessible locations without jeopardizing people’s safety. On-going work includes implementation into a commercially available drone.

Towards a holistic framework for the evaluation of emergency plans in indoor environments

One of the most promising fields for ambient intelligence is the implementation of intelligent emergency plans. Because the use of drills and living labs cannot reproduce social behaviors, such as panic attacks, that strongly affect these plans, the use of agent-based social simulation provides an approach to evaluate these plans more thoroughly. (1) The hypothesis presented in this paper is that there has been little interest in describing the key modules that these simulators must include, such as formally represented knowledge and a realistic simulated sensor model, and especially in providing researchers with tools to reuse, extend and interconnect modules from different works. This lack of interest hinders researchers from achieving a holistic framework for evaluating emergency plans and forces them to reconsider and to implement the same components from scratch over and over. In addition to supporting this hypothesis by considering over 150 simulators, this paper: (2) defines the main modules identified and proposes the use of semantic web technologies as a cornerstone for the aforementioned holistic framework; (3) provides a basic methodology to achieve the framework; (4) identifies the main challenges; and (5) presents an open and free software tool to hint at the potential of such a holistic view of emergency plan evaluation in indoor environments.