Disaster Transmission Network Based on Next-Generation Video Standard

Zhou, Jianguo; Yi, Shuwen; Yang, Jingrun; Zhou, Liang; Lu, Lu

doi:10.1109/icicas48597.2019.00037

Cited by 1 publication

(1 citation statement)

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“…Research on disaster prevention is mainly used for early warning of abnormal crowd behavior associated with man-made disasters, such as crowd abnormal motion detection [12] and scene population statistics [13]. In disaster emergency response, relevant research mainly integrates the video transmitted by unmanned aerial vehicle [14,15], balloon [16], satellite [17], and other platform load cameras with GIS information to evaluate and analyze the disaster situation. For specific research, one mainly focuses on the system construction level, and the other mainly focuses on the data analysis level.…”

Section: Related Workmentioning

confidence: 99%

Detection of Multicamera Pedestrian Trajectory Outliers in Geographic Scene

Wang

Xie

Wang

2022

Wireless Communications and Mobile Computing

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The abnormal detection of moving objects in intelligent video surveillance system plays an important role in early warning for man-made disasters. However, the current abnormal detection methods cannot effectively perceive the cross-camera abnormal movements of video objects. The main reason is that the existing methods ignore the spatial relationship between the fields of view of different cameras and blind areas among the fields of view. This condition prevents them to effectively infer and analyze the cross-camera movements of video objects combined with geospatial information. This paper proposes the detection of multicamera pedestrian trajectory outliers in geographic scene to address this problem. This approach first spatializes the video object trajectory and then realizes trajectory vectorization by extracting trajectory points with equal time difference. The position trajectory outliers are detected by constructing isolation forest and scoring trajectory vectors, and the velocity trajectory outliers are identified through vectors’ neighborhood comparison. Related experiments show that our method can effectively improve the efficiency and accuracy of detecting trajectory outliers, which can enhance the early warning capability of video surveillance systems for man-made disasters.

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