Emergency Detection Based on Motion History Image and AdaBoost for an Intelligent Surveillance System
This paper proposes a method to detect emergency situations in a video stream using a Motion History Image (MHI) and AdaBoost for a video-based intelligent surveillance system. The proposed method creates a MHI of each human object through an image processing technique entailing background removal based on Gaussian Mixture Model (GMM) followed by labeling and accumulating the foreground images. The obtained MHI is then compared with the existing MHI templates to detect emergency situations. To evaluate the proposed emergency detection method, a set of experiments on a dataset of video clips captured from a surveillance camera were conducted. The results show that we successfully detected emergency situations using the proposed method.
KeywordsGaussian mixture model Motion history image Video-based surveillance system AdaBoost
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2011-0013776). This work was also supported by the NAP (National Agenda Project) of the Korea Research Council of Fundamental Science & Technology.
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