Open AccessCenterNet-Triplets application: surveillance camera illegal management detection
Author(s) -
Xiangxiang Zhu,
Yang Li,
Jianhua Zhang,
Ping Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012091
Subject(s) - computer science , object detection , computer security , order (exchange) , artificial intelligence , business , pattern recognition (psychology) , finance
There are two types of illegal business in city management: out-of-store management and illegal mobile management, we simply refer to as outmanagement and flowmagement. The main way to deal with illegal buessiness is manual management. However, this requires a lot of costs, and it is impossible for city management personnel to conduct 24hour inspections. In order to improve management efficiency and save labor costs, this article uses city surveillance cameras, combined with deep learning object detection methods, to conduct real-time detection of illegal operations. Aiming at the situation where the amount of surveillance video data is small, a data enhancement method is proposed to improve the detection effect. CenterNet-Triplets is a object detection algorithm without anchor boxes. Its backbone network is Hourglass Network. The AP of CenterNet-Triplets on MS-COCO dataset is 47.0%, and it has a good detection speed. Experimental results show that CenterNet-Triplets is suitable as a method for detecting illegal operations in city management.