Image-Based Fire Detection Using Neural Networks

[[abstract]]An image-based fire detection method using neural networks is proposed in this paper. First, flame color features, based on the HSI color model, are trained by a backpropagation neural network for flame recognition. Then, based on the learned flame color features, regions with fire-like colors are roughly separated from an image. Besides segmenting flame regions, background objects with similar fire colors or resulted from the reflection of fire flames are also separated from the image. In order to get rid of these spurious fire-like regions, the image difference method and the invented color masking technique are applied. Finally, a compact method is devised to estimate the burning degree of fire flames so that users could be informed with a proper warning alarm. The proposed system can achieve 96.47% fire detection rate on average.[[sponsorship]]高雄應用科技大學 JCIS[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20061008~20061011[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]高雄, 臺

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

[EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. 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Symmetry, 10(9), 375. doi:10.3390/sym10090375Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679-3689. doi:10.1109/tii.2018.2791944Barthélemy, J., Verstaevel, N., Forehead, H., & Perez, P. (2019). Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors, 19(9), 2048. doi:10.3390/s19092048Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., & Altowaijri, S. M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19(9), 2206. doi:10.3390/s19092206Xu, S., Zou, S., Han, Y., & Qu, Y. (2018). Study on the Availability of 4T-APS as a Video Monitor and Radiation Detector in Nuclear Accidents. Sustainability, 10(7), 2172. doi:10.3390/su10072172Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). 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Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding