Commenced in January 2007
Paper Count: 30836
Challenges in Video Based Object Detection in Maritime Scenario Using Computer Vision
Abstract:This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128231Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 846
 D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabaly, and C. Quek, “Video processing from electro-optical sensors for object detection and tracking in maritime environment: A Survey,” Intelligent Transportation Systems, IEEE Transactions on, 2017.
 S. Fefilatyev, D. Goldgof, M. Shreve, and C. Lembke, “Detection and tracking of ships in open sea with rapidly moving buoy-mounted camera system,” Ocean Engineering, vol. 54, pp. 1–12, 2012.
 D. D. Bloisi, L. Iocchi, A. Pennisi, and L. Tombolini, “ARGOS-Venice boat classification,” in Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on, 2015, pp. 1–6.
 D. K. Prasad, D. Rajan, C. Prasath, L. Rachmawati, E. Rajabaly, and C. Quek, “MSCM-LiFe: multi-scale cross modal linear feature for horizon detection in maritime images,” in IEEE TENCON, 2016.
 S. M. Ettinger, M. C. Nechyba, P. G. Ifju, and M. Waszak, “Vision-guided flight stability and control for micro air vehicles,” Advanced Robotics, vol. 17, no. 7, pp. 617–640, 2003.
 D. K. Prasad, M. K. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image and Vision Computing, vol. 30, no. 11, pp. 843–859, 2012.
 D. K. Prasad and M. K. Leung, Polygonal representation of digital curves. INTECH Open Access Publisher, 2012.
 D. K. Prasad, M. K. Leung, C. Quek, and M. S. Brown, “DEB: Definite error bounded tangent estimator for digital curves,” IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4297–4310, 2014.
 D. K. Prasad and M. S. Brown, “Online tracking of deformable objects under occlusion using dominant points,” JOSA A, vol. 30, no. 8, pp. 1484–1491, 2013.
 D. K. Prasad, “Fabrication imperfection analysis and statistics generation using precision and reliability optimization method,” Optics express, vol. 21, no. 15, pp. 17 602–17 614, 2013.
 D. Cheng, D. K. Prasad, and M. S. Brown, “Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution,” JOSA A, vol. 31, no. 5, pp. 1049–1058, 2014.
 D. K. Prasad and L. Wenhe, “Metrics and statistics of frequency of occurrence of metamerism in consumer cameras for natural scenes,” JOSA A, vol. 32, no. 7, pp. 1390–1402, 2015.
 S. Fefilatyev, V. Smarodzinava, L. O. Hall, and D. B. Goldgof, “Horizon detection using machine learning techniques,” in International Conference on Machine Learning and Applications, 2006, pp. 17–21.
 D. K. Prasad and K. Agarwal, “Classification of hyperspectral or trichromatic measurements of ocean color data into spectral classes,” Sensors, vol. 16, no. 3, p. 413, 2016.
 R. Behringer, “Registration for outdoor augmented reality applications using computer vision techniques and hybrid sensors,” in Virtual Reality, 1999, pp. 244–251.
 X. Cao, Z. Rasheed, H. Liu, and N. Haering, “Automatic geo-registration of maritime video feeds,” in International Conference on Pattern Recognition, 2008, pp. 1–4.
 A. Criminisi and A. Zisserman, “Shape from Texture: Homogeneity Revisited,” in British Machine Vision Conference, 2000, pp. 1–10.
 S. Fefilatyev, “Algorithms for visual maritime surveillance with rapidly moving camera,” Ph.D. dissertation, University of South Florida, 2012.
 D. Dusha, W. Boles, and R. Walker, “Attitude estimation for a fixed-wing aircraft using horizon detection and optical flow,” in Digital Image Computing Techniques and Applications, 2007, pp. 485–492.
 S. Y. Elhabian, K. M. El-Sayed, and S. H. Ahmed, “Moving object detection in spatial domain using background removal techniques-state-of-art,” Recent patents on computer science, vol. 1, no. 1, pp. 32–54, 2008.
 V. Ablavsky, “Background models for tracking objects in water,” in International Conference on Image Processing, vol. 3, 2003, pp. III–125.
 A. Sobral and A. Vacavant, “A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos,” Computer Vision and Image Understanding, vol. 122, pp. 4–21, 2014.
 Y. Wang, P.-M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, “Cdnet 2014: an expanded change detection benchmark dataset,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014, pp. 387–394.
 Z. Zivkovic and F. van der Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognition Letters, vol. 27, no. 7, pp. 773–780, 2006.
 C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780–785, 1997.
 P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin, “Subsense: A universal change detection method with local adaptive sensitivity,” Image Processing, IEEE Transactions on, vol. 24, no. 1, pp. 359–373, 2015.
 D. Bloisi, L. Iocchi, M. Fiorini, and G. Graziano, “Automatic maritime surveillance with visual target detection,” in Proc. of the International Defense and Homeland Security Simulation Workshop, 2011, pp. 141–145.