Convolutional Neural Networks Based Fire Detection in Surveillance Videos
By Khan Muhammad, Jamil Ahmad, I. Mehmood, Seungmin Rho, Sung Wook Baik. IEEE Access, 2018
448 citations recorded by the source corpus.
The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research paper, we propose a cost-effective fire detection CNN architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as AlexNet. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/037e0333b776a6b39decd3d269630c27cc5558a3
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Citations in the corpus
- A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing (2020, methodology): “[52], inspired by GoogleNet architecture and proposed a fine-tuned fire detection CNN model for surveillance videos, while Shen et al.”
- Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications (2019, unknown): “detection and localization [28], [29], image segmentation, super-resolution, classification [30]–[33], and indexing and retrieval [34].”