Content-oriented Multicamera Trajectory Forecasting Surveillance Network System
Xin Qi, Toshio Sato, Keping Yu, San Hlaing Myint, Yutaka Katsuyama, Kazuhiko Tamesue, Kiyohito Tokuda, Zheng Wen, Takuro Sato · 2021
To reduce safety violations in wide-area ranges, there is a need for highly functional multicamera surveillance systems. We introduce a multicamera trajectory forecasting surveillance network system based on a content-oriented suspicious object network system. This system uses multiple cameras in detection and recognition to track persons among different areas and is capable of retracking people. Each camera node has a processing unit and uses information-centric networking technology to build a content-oriented IoT network. We use field-recorded data to support the simulation, and the evaluation result indicates that our trajectory forecasting method is more efficient than conventional surveillance systems.