ZTE

ZTE Communications ›› 2015, Vol. 13 ›› Issue (2): 23-27.DOI: 10.3969/j.issn.1673-5188.2015.02.005

• Special Topic • Previous Articles     Next Articles

A Parameter-Detection Algorithm for Moving Ships

Yaduan Ruan, Juan Liao, Jiang Wang, Bo Li, Qimei Chen   

  1. School of Electronic and Engineering, Nanjing University, Nanjing 210093, China
  • Received:2015-03-29 Online:2015-06-25 Published:2015-06-25
  • About author:Yaduan Ruan (ruanyaduan@163.com) received the PhD degree from Nanjing University in 2014. She is a lecturer at School of Electronic Science and Engineering, Nanjing University. Her research interests include image processing and machine learning.
    Juan Liao (liaojuan308@163.com) received the BS and MS degrees from Anhui University of Science and Technology in 2008 and 2011. She is currently a PhD candidate at Nanjing University. Her research interests include computer vision and image processing.
    Jiang Wang (jiangnju_edu@sina.com) received the BS degree from Nanjing University in 2012. He is an MS degree candidate at Nanjing University. His research interests include computer vision and image processing.
    Bo Li (liboee@nju_edu.cn) received the PhD degree in signal and information processing from Nanjing University in 2009. He is an associate professor at Nanjing University. His research interests include computer vision and machine learning.
    Qimei Chen (chenqimei@nju_edu.cn) received the MS degree from Tsinghua University, Beijing, in 1982. He is a professor at Nanjing University. His research interests include visual surveillance, image and video processing.
  • Supported by:
    This work was supported by Fund of National Science & Technology monumental projects under Grants NO.61401239, NO.2012-364-641-209.

A Parameter-Detection Algorithm for Moving Ships

Yaduan Ruan, Juan Liao, Jiang Wang, Bo Li, Qimei Chen   

  1. School of Electronic and Engineering, Nanjing University, Nanjing 210093, China
  • 作者简介:Yaduan Ruan (ruanyaduan@163.com) received the PhD degree from Nanjing University in 2014. She is a lecturer at School of Electronic Science and Engineering, Nanjing University. Her research interests include image processing and machine learning.
    Juan Liao (liaojuan308@163.com) received the BS and MS degrees from Anhui University of Science and Technology in 2008 and 2011. She is currently a PhD candidate at Nanjing University. Her research interests include computer vision and image processing.
    Jiang Wang (jiangnju_edu@sina.com) received the BS degree from Nanjing University in 2012. He is an MS degree candidate at Nanjing University. His research interests include computer vision and image processing.
    Bo Li (liboee@nju_edu.cn) received the PhD degree in signal and information processing from Nanjing University in 2009. He is an associate professor at Nanjing University. His research interests include computer vision and machine learning.
    Qimei Chen (chenqimei@nju_edu.cn) received the MS degree from Tsinghua University, Beijing, in 1982. He is a professor at Nanjing University. His research interests include visual surveillance, image and video processing.
  • 基金资助:
    This work was supported by Fund of National Science & Technology monumental projects under Grants NO.61401239, NO.2012-364-641-209.

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Abstract: In traffic-monitoring systems, numerous vision-based approaches have been used to detect vehicle parameters. However, few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object. In this paper, we present an approach to detecting the parameters of a moving ship in an inland river. This approach involves interactive calibration without a calibration reference. We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios, and we detect ship length, width, speed, and flow. We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90% for all parameters.

Key words: video analysis, interactive calibration, foreground detection algorithm, traffic parameter detection

摘要： In traffic-monitoring systems, numerous vision-based approaches have been used to detect vehicle parameters. However, few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object. In this paper, we present an approach to detecting the parameters of a moving ship in an inland river. This approach involves interactive calibration without a calibration reference. We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios, and we detect ship length, width, speed, and flow. We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90% for all parameters.

关键词: video analysis, interactive calibration, foreground detection algorithm, traffic parameter detection