1,014 research outputs found
An intelligent video fire detection approach based on object detection technology
PresentationFire that is one of the most serious accidents in chemical factories, may lead to considerable product losses, equipment damages and casualties. With the rapid development of computer vision technology, intelligent fire detection has been proposed and applied in various scenarios. This paper presents a new intelligent video fire detection approach based on object detection technology using convolutional neural networks (CNN). First, a CNN model is trained for the fire detection task which is framed as a regression problem to predict bounding boxes and associated probabilities. In the application phase, videos from surveillance cameras are detected frame by frame. Once fire appears in the current frame, the model will output the coordinates of the fire region. Simultaneously, the frame where the fire region is localized will be immediately sent to safety supervisors as a fire alarm. This will help detect fire at the early stage, prevent fire spreading and improve the emergency response
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
Particle-Filter-Based Intelligent Video Surveillance System
In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience
Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns
AbstractThe early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96–99% and 86–94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise
CUDA based implementation of flame detection algorithms in day and infrared camera videos
Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 52-54.Automatic fire detection in videos is an important task but it is a challenging
problem. Video based high performance fire detection algorithms are important
for the detection of forest fires. The usage area of fire detection algorithms can
further be extended to the places like state and heritage buildings, in which
surveillance cameras are installed. In uncontrolled fires, early detection is crucial
to extinguish the fire immediately. However, most of the current fire detection
algorithms either suffer from high false alarm rates or low detection rates due
to the optimization constraints for real-time performance. This problem is also
aggravated by the high computational complexity in large areas, where multicamera
surveillance is required. In this study, our aim is to speed up the existing
color video fire detection algorithms by implementing in CUDA, which uses the
parallel computational power of Graphics Processing Units (GPU). Our method
does not only speed up the existing algorithms but it can also reduce the optimization
constraints for real-time performance to increase detection probability
without affecting false alarm rates. In addition, we have studied several methods
that detect flames in infrared video and proposed an improvement for the
algorithm to decrease the false alarm rate and increase the detection rate of the
fire.Hamzaçebi, HasanM.S
Image-Based Fire Detection in Industrial Environments with YOLOv4
Fires have destructive power when they break out and affect their
surroundings on a devastatingly large scale. The best way to minimize their
damage is to detect the fire as quickly as possible before it has a chance to
grow. Accordingly, this work looks into the potential of AI to detect and
recognize fires and reduce detection time using object detection on an image
stream. Object detection has made giant leaps in speed and accuracy over the
last six years, making real-time detection feasible. To our end, we collected
and labeled appropriate data from several public sources, which have been used
to train and evaluate several models based on the popular YOLOv4 object
detector. Our focus, driven by a collaborating industrial partner, is to
implement our system in an industrial warehouse setting, which is characterized
by high ceilings. A drawback of traditional smoke detectors in this setup is
that the smoke has to rise to a sufficient height. The AI models brought
forward in this research managed to outperform these detectors by a significant
amount of time, providing precious anticipation that could help to minimize the
effects of fires further.Comment: Accepted for publication at ICPRA
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