Performance of optical flow techniques
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Smoke Detection for Early Fire-Alarming System Base on Video Processing
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Real-time detection of steam in video images
Pattern Recognition
A medical tracking system for contrast media
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Transmission: a new feature for computer vision based smoke detection
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Early recognition of smoke in digital video
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Fire surveillance method based on quaternionic wavelet features
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Digital Signal Processing
Smoke Detection in Video: An Image Separation Approach
International Journal of Computer Vision
Early smoke detection in video using swaying and diffusion feature
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Video smoke detection has many advantages over traditional methods, such as fast response, non-contact, and so on. But most of video smoke detection systems usually have high false alarms. In order to improve the performance of video smoke detection, we propose an accumulative motion model based on the integral image by fast estimating the motion orientation of smoke. But the estimation is not very precise due to block sum. Not very accurate estimation will affect the subsequent decision. To reduce this influence, the accumulation of the orientation over time is performed to compensate results for the inaccuracy of orientation. The model is able to mostly eliminate the disturbance of artificial lights and non-smoke moving objects by using the accumulation of motion. The model together with chrominance detection can correctly detect the existence of smoke. Experimental results show that our algorithm has good robustness for smoke detection.