Rights statement: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Accepted author manuscript, 871 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 1/04/2018 |
|---|---|
| <mark>Journal</mark> | IEEE Internet of Things Journal |
| Issue number | 2 |
| Volume | 5 |
| Number of pages | 8 |
| Pages (from-to) | 747-754 |
| Publication Status | Published |
| Early online date | 18/05/17 |
| <mark>Original language</mark> | English |
Automated object detection algorithm is an important research challenge in intelligent urban surveillance systems for Internet of Things (IoT) and smart cities applications. In particular, smart vehicle license plate recognition and vehicle detection are recognized as core research issues of these IoT-driven intelligent urban surveillance systems. They are key techniques in most of the traffic related IoT applications, such as road traffic real-time monitoring, security control of restricted areas, automatic parking access control, searching stolen vehicles, etc. In this paper, we propose a novel unified method of automated object detection for urban surveillance systems. We use this novel method to determine and pick out the highest energy frequency areas of the images from the digital camera imaging sensors, that is, either to pick the vehicle license plates or the vehicles out from the images. Our proposed method can not only help to detect object vehicles rapidly and accurately, but also can be used to reduce big data volume needed to be stored in urban surveillance systems.