Smoke Detection System Based on Temporal Spatial and Spatio-Temporal Analysis

• Main Authors: Huang, Han-Wen, 黃瀚文
• Other Authors: Lin, Chin-Teng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/45ht2e

碩士 === 國立交通大學 === 影像與生醫光電研究所 === 102 === Smoke always accompanies with fires as a early sign. Hence, it's practical to send alerts to prevent fire disasters. Visual-based smoke detection techniques in surveillance systems have been studied for years. However, there are some challenging problems to detect smoke within a short reaction time, recognize non-smoke objects correctly and decrease the false alarm rate. This study presents temporal spatial and spatio-temporal analysis on image sequences. High-pass filter and low-pass filter are exploited on both domain to generate eight features by cross combinations. This approach can extract information that smoke moves swiftly with time and changes slowly on spatial domain. In order to obtain the proper generalization ability with respect to sparse training samples, a support vector machine is to combine the eight features as a classifier. The global verifications including area ratio, area spread and spatial variance is used to decrease false alarm. Experimental results show the false alarm rate is lower and the reaction time is shorter than other approaches. The proposed approach in this study can detect smoke without background modeling and provide better performance in the field of smoke detection.