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Conference Paper Pedestrian Video Data Abstraction and Classification for Surveillance System
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Authors
Ho-chul Shin, Jae-yeong Lee
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1476-1478
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539426
Abstract
In this study, we have developed abstracted pedestrian behavior representation and classification method for pedestrian video surveillance system. An effective intelligent surveillance system can be constructed if the high-resolution surveillance image information is efficiently summarized. The motion of the pedestrian is represented by a multi-layer grid map using a detector and a tracker. A normal pattern and anomalous pattern database were constructed and classified using the CNN classifier. With the abstracted pedestrian data and CNN network, the abnormal situation can be detected up to recall 92.0%, precision 99.9%.
KSP Keywords
Abnormal situation, Behavior representation, Classification method, Grid Map, High resolution, Image information, Intelligent surveillance system, Normal Pattern, Pattern database, Video data, Video surveillance system