| Title |
Multiple Objects Association System For The Smart City |
| ID_Doc |
38622 |
| Authors |
Popov A.Y.; Ibragimov S.V.; Malyshev S.A.; Abdurakhmanova R.A. |
| Year |
2021 |
| Published |
Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021 |
| DOI |
http://dx.doi.org/10.1109/ElConRus51938.2021.9396415 |
| Abstract |
The increase of population in large cities require safety systems improvement for urban environment. The traditional Smart City security systems are highly centralized to process a large number of video streams in the massive-parallel data centers. As a result, this requires an expensive network infrastructure and makes it available only for large metropolitan areas. In this paper, we propose a distributed system for analyzing the luggage and people relations in crowded places. The system processes video on the camera's nodes and detects dependent tracks for objects moving. Then we form a combined scene in the central node to accurately represent the association of people and luggage. In this research stage, we present the basics concepts of multiple object re-identification and association measurement, as well as knowledge graph constructing. We describe the methodology of data obtaining and show experimental results of visual object recognition on camera nodes. © 2021 IEEE. |
| Author Keywords |
convolutional neural network; deep learning; knowledge graph; mart city; multiple objects tracking; object detection; objects association; re-identification |
