Challenges in Video Based Object Detection in Maritime Scenario Using Computer Vision

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.




References:
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] D. K. Prasad and M. K. Leung, Polygonal representation of digital
curves. INTECH Open Access Publisher, 2012.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] R. Behringer, “Registration for outdoor augmented reality applications
using computer vision techniques and hybrid sensors,” in Virtual Reality,
1999, pp. 244–251.
[16] 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.
[17] A. Criminisi and A. Zisserman, “Shape from Texture: Homogeneity
Revisited,” in British Machine Vision Conference, 2000, pp. 1–10.
[18] S. Fefilatyev, “Algorithms for visual maritime surveillance with rapidly
moving camera,” Ph.D. dissertation, University of South Florida, 2012.
[19] 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.
[20] 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.
[21] V. Ablavsky, “Background models for tracking objects in water,” in
International Conference on Image Processing, vol. 3, 2003, pp. III–125.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.