Abstract
In modern traffic systems, accurate video detection is a key challenge for traffic management. Aiming at the problem of public bus detection, this paper proposes a video detection method to well recognize the buses. Firstly, we employ the foreground detection method to find the moving vehicles. And then a training classifier which consists of the improved Adaboost algorithm and Haar-like features is proposed to filter undesired vehicles. Secondly, we use the Canny operator to locate bus characteristics, and further detect the bus with the modified HSV model. This design is tested on the Visual Stadio and OpenCV platform in which load the urban transport data as the samples. The test results show that our detection method has better robustness than both three-frame differential method and hybrid Gaussian method, and the accuracy of detection on the window positioning is more than 93 percent.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2328-2331 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781467389778 |
| DOIs | |
| Publication status | Published - 2017 Sept 29 |
| Event | 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 - Chongqing, China Duration: 2017 Mar 25 → 2017 Mar 26 |
Other
| Other | 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 |
|---|---|
| Country/Territory | China |
| City | Chongqing |
| Period | 17/3/25 → 17/3/26 |
Keywords
- Adaboost algorithm
- HSV model
- Intelligent traffic
- Video detection
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Information Systems
- Electrical and Electronic Engineering
- Control and Optimization
- Information Systems and Management