Convolutional Neural Networks Based Fire Detection in Surveillance Videos

Muhammad, Khan; Ahmad, Jamil; Mehmood, Irfan; Rho, Seungmin

doi:10.1109/access.2018.2812835

Cited by 427 publications

(194 citation statements)

References 32 publications

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“…However, the complexity of the proposed technique in [27] limits the real-time algorithmic execution on a Raspberry Pi embedded system to only 3 fps and using only low-resolution images, 320 × 240 pixels. A convolutional neural networks (CNN) approach for video-based antifire surveillance systems was proposed in [18,28], but also in this case the implementation complexity, although reduced compared to other CNN-based techniques, is still too high. For example, [28] required a system equipped with a NVidia GeForce GTX TITAN X with 12 GB onboard memory and deep learning framework and Intel Core i5 CPU with 64 GB RAM.…”

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

“…A convolutional neural networks (CNN) approach for video-based antifire surveillance systems was proposed in [18,28], but also in this case the implementation complexity, although reduced compared to other CNN-based techniques, is still too high. For example, [28] required a system equipped with a NVidia GeForce GTX TITAN X with 12 GB onboard memory and deep learning framework and Intel Core i5 CPU with 64 GB RAM. Such a platform has a cost in the order of hundreds of USD with a power consumption of hundreds of Watts.…”

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

“…Other papers, like [19], implement more sophisticated video processing techniques, like Kalman based motion estimation, but with fixed parameters not aware of the specific camera configuration. Techniques such as in [18,28], based on CNNs, are difficult to implement in real scenarios due to the lack of a large dataset for the training videos. This work also outperforms a preliminary algorithm, studied and presented by the authors in [22,23], which was using SAD (sum of absolute differences) block matching as motion estimation, plus other image processing tasks, such as morphological filter, bounding boxes extraction, features extraction, correct bounding boxes selection, and space and time analysis.…”

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

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

Gagliardi

Saponara

2020

Energies

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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.

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Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

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

Gagliardi

Saponara

2020

Energies

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“…Instead of assessing image repetitively as in CNN, image is scanned once for all, thereby increasing the processing of frames per second (fps). YOLO is trained based on loss occurred unlike the traditional Classification approach [7]. Paper describes about video analytics part for road traffic.…”

Section: Literature Surveymentioning

confidence: 99%

Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications

Mohana¹,

Aradhya²

2019

IJACSA

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Data is the new oil in current technological society. The impact of efficient data has changed benchmarks of performance in terms of speed and accuracy. The enhancement is visualizable because the processing of data is performed by two buzzwords in industry called Computer Vision (CV) and Artificial Intelligence (AI). Two technologies have empowered major tasks such as object detection and tracking for traffic vigilance systems. As the features in image increases demand for efficient algorithm to excavate hidden features increases. Convolution Neural Network (CNN) model is designed for urban vehicle dataset for single object detection and YOLOv3 for multiple object detection on KITTI and COCO dataset. Model performance is analyzed, evaluated and tabulated using performance metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Accuracy, Precision, confusion matrix and mean Average Precession (mAP). Objects are tracked across the frames using YOLOv3 and Simple Online Real Time Tracking (SORT) on traffic surveillance video. This paper upholds the uniqueness of the state of the art networks like DarkNet. The efficient detection and tracking on urban vehicle dataset is witnessed. The algorithms give real-time, accurate, precise identifications suitable for realtime traffic applications.

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“…The most popular approach is based on methods of deep learning algorithms and convolutional neural networks in these days [13][14][15][16][17]. This approach usually requires no feature extraction, but the final neural networks are significantly more complex and more computational resources are requested.…”

Section: Usage Of Artificial Neural Networkmentioning

confidence: 99%

Fire Detection in Video Stream by Using Simple Artificial Neural Network

Janku

Oplatková

Dulík

et al. 2018

mendel

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This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.

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Convolutional Neural Networks Based Fire Detection in Surveillance Videos

Cited by 427 publications

References 32 publications

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

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

Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications

Fire Detection in Video Stream by Using Simple Artificial Neural Network

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