Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection

Dimitropoulos, Kosmas; Barmpoutis, Panagiotis; Grammalidis, Nikos

doi:10.1109/tcsvt.2014.2339592

Cited by 188 publications

(74 citation statements)

References 24 publications

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“…Computation time comparison of [22,23] and AdViSED. Tables 3 and 4 compare the proposed smoke detection technique to the state of the art [20,21,[36][37][38][39][40][41][42][43][44] using the set of test videos, which is the one showed in Figure 8. The results in Table 3 show that AdViSED has improved recognition capabilities in terms of correctly detected frames and has no false alarms.…”

Section: Performance and Complexity Resultsmentioning

confidence: 99%

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: Performance and Complexity Resultsmentioning

confidence: 99%

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

Gagliardi

Saponara

2020

Energies

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“…Because analyzing the dynamic movement of a fire flame is an important factor in improving the fire detection performance, Dimitropoulos et al [22] used an SVM classifier and the spatio-temporal consistency energy of each candidate fire region by exploiting prior knowledge regarding the possible existence of a fire in the neighboring blocks based on the current and previous frames. In addition, Foggia et al [9] proposed a multi-expert system combining complementary feature information based on the color, shape variation and a motion analysis.…”

Section: Machine Learning and Deep Learning-based Fire Detectionmentioning

confidence: 99%

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Park

2020

Sensors

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While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

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“…This combination is employed in the work of Dimitropoulos (Dimitropoulos et al, 2014), which represents each frame according to the most prominent texture and shape features. It also combines such representation with spatio-temporal motion features to employ SVM to detect fire in videos.…”

Section: Related Workmentioning

confidence: 99%

Techniques for Effective and Efficient Fire Detection from Social Media Images

Bedo

Blanco

Oliveira

et al. 2015

Proceedings of the 17th International Conference on Enterprise Information Systems

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Abstract:Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFireDt), which combines feature extractor and evaluation functions to support instance-based learning; (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -the Flickr-Fire dataset; and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFireDt was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.

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Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection

Cited by 188 publications

References 24 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

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Techniques for Effective and Efficient Fire Detection from Social Media Images

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