TAILIEUCHUNG - Đề tài " Video Fire Smoke Detection Using Motion and Color Features "

A novel video smoke detection method using both color and motion features is presented. The result of optical flow is assumed to be an approximation of motion field. Background estimation and color-based decision rule are used to determine candidate smoke regions. The Lucas Kanade optical flow algorithm is proposed to calculate the optical flow of candidate regions. And the motion features are calculated from the optical flow results and use to differentiate smoke from some other moving objects. Finally, a back-propagation neural network is used to classify the smoke features from non-fire smoke features. Experiments show that the algorithm is significant for improving the accuracy of video smoke. | X Fire Technology 2009 Springer Science Business Media LLC. Manufactured in The United States DOI s10694-009-0110-z Video Fire Smoke Detection Using Motion and Color Features Yu Chunyu Fang Jun Wang Jinjun and Zhang Yongming State Key Laboratory of Fire Science USTC Number 96 Jin Zhai Road Hefei Anhui China e-mail ycyu@ fangjun@ wangjinj@ Received 9 July 2009 Accepted 29 September 2009 Abstract. A novel video smoke detection method using both color and motion features is presented. The result of optical flow is assumed to be an approximation of motion field. Background estimation and color-based decision rule are used to determine candidate smoke regions. The Lucas Kanade optical flow algorithm is proposed to calculate the optical flow of candidate regions. And the motion features are calculated from the optical flow results and use to differentiate smoke from some other moving objects. Finally a back-propagation neural network is used to classify the smoke features from non-fire smoke features. Experiments show that the algorithm is significant for improving the accuracy of video smoke detection and reducing false alarms. Keywords Video smoke detection Fire detection Motion features Optical flow Neural network 1. Introduction Conventional point-type thermal and smoke detectors are widely used nowadays but they typically take charge of a limited area in space. In large rooms and high buildings it may take a long time for smoke particles and heat to reach a detector. Video-based fire detection VFD is a newly developed technique in the last few years and it can greatly serve the fire detection requirement in large rooms and high buildings and even outdoor environment. Researchers all over the world have done a lot of work on this new technique. Up to now most of methods make use of the visual features of fire including color textures geometry flickering and motion. Early studies began with video flame detection using .

• Báo cáo khoa học
• Video smoke detection
• Fire detection
• Motion features
• Optical flow
• Neural network
• Color Features
• Tạp chí tin học
• Điều khiển học
• Smoke detection
• Video sequences
• Background subtraction
• Weber contrast analysis
• International journal of computer science & communication networks
• Image retrieval using combined features of color
• Texture features with SVM classification
• Combined features of color
• The query image
• Content based image retrieval
• Visual descriptors
• Gradient Local Auto Correlations
• Shape features
• Novel visual content descriptor using color
• An Approach to Cbir using K means Clustering
• Polygon based shape features
• Segment an image
• Color and shape information
• Báo cáo sinh học hay
• cách trình bày báo cáo
• báo cáo sinh học
• công trình nghiên cứu sinh học
• tài liệu về sinh học
• báo cáo khoa học
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• International Journal of Computer Networks and Communications Security
• Construct smartphone application
• Produce photo mosaic using client server model
• Statistical extracted color features
• The K means clustering