Abstract
In order to recognize the abnormal event, such as emergency or panic, happened in public scenes timely, an algorithm based on features extraction and BP Adaboost to detect abnormal frame event from surveillance video of complex situation is proposed. The proposed method detects an abnormal event where people are running, and this panic situation is simulated by the frame in a video. Experiments show that the method can distinguish and detect the abnormal event effectively and efficiently, which has potentiality to be used in the real public monitoring.
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Acknowledgement
This work is partially supported by the ANR AutoFerm project (agence nationale de la recherche, Auto-ferme) and the Platform CAPSEC (capteurs pour la sécurité) funded by Région Champagne-Ardenne and FEDER (fonds européen de développement régional), the Fundamental Research Funds for the Central Universities and the National Natural Science Foundation of China (Grant No. U1435220, 61503017, 61365003), Gansu Province Basic Research Innovation Group Project (1506RJIA031).
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Zhang, Y., Wang, T., Qiao, M., Zhu, A., Li, C., Snoussi, H. (2016). Detection of Abnormal Event in Complex Situations Using Strong Classifier Based on BP Adaboost. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_21
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DOI: https://doi.org/10.1007/978-3-319-42294-7_21
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