Abnormal Event Detection Method in Multimedia Sensor Networks
Detecting abnormal events in multimedia sensor networks (MSNs) plays an increasingly essential role in our lives. Once video cameras cannot work (e.g., the sightline is blocked), audio sensor can provide us with critical information (e.g., in detecting the sound of gun-shot in the rainforest or the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/154658 |
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| _version_ | 1849765434273824768 |
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| author | Qi Li Xiaoming Liu Xinyu Yang Ting Li |
| author_facet | Qi Li Xiaoming Liu Xinyu Yang Ting Li |
| author_sort | Qi Li |
| collection | DOAJ |
| description | Detecting abnormal events in multimedia sensor networks (MSNs) plays an increasingly essential role in our lives. Once video cameras cannot work (e.g., the sightline is blocked), audio sensor can provide us with critical information (e.g., in detecting the sound of gun-shot in the rainforest or the sound of car accident on a busy road). Audio sensors also have price advantage. Detecting abnormal audio events in complicated background environment is a very difficult problem; only few previous researches could offer good solution. In this paper, we proposed a novel method to detect the unexpected audio elements in multimedia sensor networks. Firstly, we collect enough normal audio elements and then use statistical learning method to train them offline. On the basis of these models, we establish a background pool by prior knowledge. The background pool contains expected audio effects. Finally, we decide whether an audio event is unexpected by comparing it with the background pool. In this way, we reduce the complexity of online training while ensuring the detection accuracy. We designed some experiments to verify the effectiveness of the proposed method. In conclusion, the experiments show that the proposed algorithm can achieve satisfying results. |
| format | Article |
| id | doaj-art-c27b4fa6d8b64f569802cf28452f722b |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c27b4fa6d8b64f569802cf28452f722b2025-08-20T03:04:51ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-11-011110.1155/2015/154658154658Abnormal Event Detection Method in Multimedia Sensor NetworksQi Li0Xiaoming Liu1Xinyu Yang2Ting Li3 Beijing University of Posts and Telecommunications, China National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100876, China Beijing University of Posts and Telecommunications, China National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100876, ChinaDetecting abnormal events in multimedia sensor networks (MSNs) plays an increasingly essential role in our lives. Once video cameras cannot work (e.g., the sightline is blocked), audio sensor can provide us with critical information (e.g., in detecting the sound of gun-shot in the rainforest or the sound of car accident on a busy road). Audio sensors also have price advantage. Detecting abnormal audio events in complicated background environment is a very difficult problem; only few previous researches could offer good solution. In this paper, we proposed a novel method to detect the unexpected audio elements in multimedia sensor networks. Firstly, we collect enough normal audio elements and then use statistical learning method to train them offline. On the basis of these models, we establish a background pool by prior knowledge. The background pool contains expected audio effects. Finally, we decide whether an audio event is unexpected by comparing it with the background pool. In this way, we reduce the complexity of online training while ensuring the detection accuracy. We designed some experiments to verify the effectiveness of the proposed method. In conclusion, the experiments show that the proposed algorithm can achieve satisfying results.https://doi.org/10.1155/2015/154658 |
| spellingShingle | Qi Li Xiaoming Liu Xinyu Yang Ting Li Abnormal Event Detection Method in Multimedia Sensor Networks International Journal of Distributed Sensor Networks |
| title | Abnormal Event Detection Method in Multimedia Sensor Networks |
| title_full | Abnormal Event Detection Method in Multimedia Sensor Networks |
| title_fullStr | Abnormal Event Detection Method in Multimedia Sensor Networks |
| title_full_unstemmed | Abnormal Event Detection Method in Multimedia Sensor Networks |
| title_short | Abnormal Event Detection Method in Multimedia Sensor Networks |
| title_sort | abnormal event detection method in multimedia sensor networks |
| url | https://doi.org/10.1155/2015/154658 |
| work_keys_str_mv | AT qili abnormaleventdetectionmethodinmultimediasensornetworks AT xiaomingliu abnormaleventdetectionmethodinmultimediasensornetworks AT xinyuyang abnormaleventdetectionmethodinmultimediasensornetworks AT tingli abnormaleventdetectionmethodinmultimediasensornetworks |