Ships Detection on Inland Waters Using Video Surveillance System

The video surveillance is used to monitor ships in order to ensure safety on waterways. The ships detection is a first step in a ship automatic identification process based on video streams. The paper presents a new algorithm for ships detection on inland waterways. The algorithm must detect moving ships of all kinds, including leisure craft, that are visible on a video stream and is designed to work for stationary cameras. Furthermore, it only requires an access to video streams from existing monitoring systems without any additional hardware or special configuration of cameras. The algorithm works in variable lightning conditions and with slight changes of background. In the paper, the test application implementing the algorithm is presented together with a series of experimental results showing the algorithm quality depending on different parameters’ sets. The main purpose of the tests was to find the optimal set of twelve parameters that will become the default setting. All moving ships, including small boats and kayaks, must be detected, which is the main difference from existing solutions that mostly focus on detection of only one vessel type. In the proposed algorithm, all objects that are moving on water are detected and then non-ships are eliminated by usage of some logic rules and excluding additional image processing methods.

[1]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[2]  Kenneth Y. Goldberg,et al.  Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation , 2012, 2012 American Control Conference (ACC).

[3]  Byung Gil Lee,et al.  Vessel tracking vision system using a combination of Kaiman filter, Bayesian classification, and adaptive tracking algorithm , 2014, 16th International Conference on Advanced Communication Technology.

[4]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[5]  Nelson F. F. Ebecken,et al.  A SURVEY ON VIDEO DETECTION AND TRACKING OF MARITIME VESSELS , 2014 .

[6]  Wu-Chih Hu,et al.  Robust real-time ship detection and tracking for visual surveillance of cage aquaculture , 2011, J. Vis. Commun. Image Represent..

[7]  Natalia Wawrzyniak,et al.  Automatic Ship Detection on Inland Waters: Problems and a Preliminary Solution , 2019, ICONS 2019.

[8]  Hélène Laurent,et al.  Comparative study of background subtraction algorithms , 2010, J. Electronic Imaging.

[9]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[10]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[11]  Jorge Branquinho,et al.  Computer Vision Algorithms Fishing Vessel Monitoring - Identification of Vessel Plate Number , 2017, ISAmI.

[12]  Zygmunt L. Szpak,et al.  Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set , 2011, Expert Syst. Appl..

[13]  Natalia Wawrzyniak,et al.  Automatic Watercraft Recognition and Identification on Water Areas Covered by Video Monitoring as Extension for Sea and River Traffic Supervision Systems , 2018 .

[14]  Takeshi Hashimoto,et al.  Examination of automatic detection and tracking of ships on camera image in marine environment , 2016, 2016 Techno-Ocean (Techno-Ocean).

[15]  Dan Xu,et al.  Background Subtraction Using Local SVD Binary Pattern , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).