An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Video Compression Standards: Algorithms and Architectures
Image and Video Compression Standards: Algorithms and Architectures
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Real-time recognition of activity using temporal templates
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Computer vision based method for real-time fire and flame detection
Pattern Recognition Letters
Fire detection using statistical color model in video sequences
Journal of Visual Communication and Image Representation
Development of early tunnel fire detection algorithm using the image processing
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
A Probabilistic Approach for Vision-Based Fire Detection in Videos
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempo-spatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate.