Abstract
Underwater video object detection is challenging because of the complex background and the movement of the camera. In order to address this, we propose a novel scheme for simultaneously estimating the camera model parameters and detecting the object. The object detection phase includes background modeling and its learning. Background is modeled by the proposed spatial kernel density estimation (SKDE) model and the model learning happens in the SKDE feature space. Background modeling and its learning is pixel-based approach. The model histograms learn the new pixel through its histogram representation. Our learning and classification strategy is different from that of the algorithm proposed by Heikkila et al. in the year 2006 in the context of similarity measure. We have developed the correntropy-based similarity measure strategy that is used for model learning and pixel classification. The camera model parameters are estimated by 2D optimization method where we have used the corner features of an object at subpixel accuracy level. These subpixel level features are used in the pipelining framework for model parameter estimation. The estimated model parameters are used to transform the input frame, which in turn is used for model learning and classification. The proposed scheme has been tested with underwater video frames from six datasets. The efficacy of the proposed scheme is compared with seven existing schemes and it is found that the proposed scheme exhibits improved performance as compared to the existing methods.
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References
Ahmed S, Khan MFR, Labib MFA, Chowdhury AE (2020) in 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE) (IEEE), pp. 117–122
Álvarez Meza A, Molina-Giraldo S, Castellanos-Dominguez G (2016) Image and Vision Computing 45:22. https://doi.org/10.1016/j.imavis.2015.11.006
Anon (2010) Whalesharks in philippines southern leyte. http://www.dvdunderwater.com
Anon (2015a) Blainville’s beaked whales. http://www.youtube.com/watch?v=SGe93WbVZJM
Anon (2015b) Montserrat south/nautilus live. http://www.youtube.com/watch?v=sPMziH2mmKQ
Anon (2016) Creepy chimaera/nautilus live. http://www.youtube.com/watch?v=jvArUKv9DvA
Bloisi DD, Pennisi A, Iocchi L (2014) Background modeling in the maritime domain. Mach Vision Appl 25(5):1257. https://doi.org/10.1007/s00138-013-0554-5
Bouwmans T (2014) Comput Sci Rev 11–12:31. https://doi.org/10.1016/j.cosrev.2014.04.001
Chen Z, Wang R, Zhang Z, Wang H, Xu L (2019) Inf Sci 483:65. https://doi.org/10.1016/j.ins.2018.12.047
Elgammal A, Harwood D, Davis L (2000) European conference on computer vision. Springer, Berlin, pp 751–767
Elgammal A, Duraiswami R, Harwood D, Davis LS (2002) Proceedings of the IEEE 90(7):1151. https://doi.org/10.1109/JPROC.2002.801448
Ge W, Guo Z, Dong Y, Chen Y (2016) Patt Recogn 59:112. https://doi.org/10.1016/j.patcog.2016.01.031
Giordano D, Palazzo S, Spampinato C (2014) in 2014 22nd International Conference on Pattern Recognition, pp. 4388–4393. https://doi.org/10.1109/ICPR.2014.751
Goyal K, Singhai J (2018) Texture-based self-adaptive moving object detection technique for complex scenes. Comput Electr Eng 70:275
Hao J, Li C, Kim Z, Xiong Z (2013) Spatio-temporal traffic scene modeling for object motion detection. IEEE Trans Intell Transp Syst 14(1):295. https://doi.org/10.1109/TITS.2012.2212432
Heikkila M, Pietikainen M (2006) IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4):657. https://doi.org/10.1109/TPAMI.2006.68
Jaffe JS (2015) IEEE J Oceanic Eng 40(3):683. https://doi.org/10.1109/JOE.2014.2350751
Kakizawa Y (2018) Nonparametric density estimation for nonnegative data, using symmetrical-based inverse and reciprocal inverse Gaussian kernels through dual transformation. J Stat Plan Inference 193:117. https://doi.org/10.1016/j.jspi.2017.08.008
Liu H, Dai J, Wang R, Zheng H, Zheng B (2016) in OCEANS 2016 - Shanghai, pp. 1–5. https://doi.org/10.1109/OCEANSAP.2016.7485613
Liu W, Pokharel PP, Principe JC (2006) in The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 4919–4924. https://doi.org/10.1109/IJCNN.2006.247192
Maity S, Chakrabarti A, Bhattacharjee D (2020) Background modeling and foreground extraction in video data using spatio-temporal region persistence features. Comput Electr Eng 81:
Miao X, Rahimi A, Rao RP (2012) Complementary kernel density estimation. Patt Recogn Lett 33(10):1381. https://doi.org/10.1016/j.patrec.2012.02.019
Migdal J, Grimson WEL (2005) in Application of Computer Vision, 2005. WACV/MOTIONS ’05 Volume 1. Seventh IEEE Workshops on 2:58–65. https://doi.org/10.1109/ACVMOT.2005.33
Mittal A, Paragios N (2004) in Computer Vision and Pattern Recognition. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, (Ieee, 2004), vol. 2, pp. II–II
Panda S, Nanda PK (2020) MRF Model-based estimation of camera parameters and detection of underwater moving objects. Int J Cogn Informatics Nat Intell (IJCINI) 14(4):1
Panda S, Nanda PK, in 2015 IEEE Underwater Technology (UT) (IEEE, 2015), pp. 1–6
Peng J, WeiDong J (2012) in 2012 5th International Congress on Image and Signal Processing, pp. 123–127. https://doi.org/10.1109/CISP.2012.6469969
Piccardi M (2004) in 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), vol. 4, pp. 3099–3104. https://doi.org/10.1109/ICSMC.2004.1400815
Powers DM (2011) arXiv preprint arXiv:2010.16061 pp. 37–63
Prabowo MR, Hudayani N, Purwiyanti S, Sulistiyanti SR, Setyawan FXA (2017) in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 1–4. https://doi.org/10.1109/EECSI.2017.8239148
Qiao Y, Xi W (2017) in Advanced Multimedia and Ubiquitous Engineering. Springer Singapore, Singapore, pp 386–392
Qiao Y, Tang Y, Li J (2013) in Measurement, Information and Control (ICMIC), 2013 International Conference on, (IEEE), vol. 2, pp. 1408–1411
Rashid M, Thomas V (2016) Procedia Technol 25:536. https://doi.org/10.1016/j.protcy.2016.08.142
Sajid H, Cheung SCS, Jacobs N (2019) Motion and appearance based background subtraction for freely moving cameras. Signal Process: Image Commun 75:11
Santamaria I, Pokharel PP, Principe JC (2006) Generalized correlation function: definition, properties, and application to blind equalization. IEEE Trans Signal Process 54(6):2187. https://doi.org/10.1109/TSP.2006.872524
Sen-ching CK, Cheung S (2004) Robust techniques for background subtraction in urban traffic video. https://doi.org/10.1117/12.526886
Singh A, Principe JC (2009) in 2009 International Joint Conference on Neural Networks, pp. 2950–2955. https://doi.org/10.1109/IJCNN.2009.5178823
Singla N (2014) Motion detection based on frame difference method. Int J Inf Comput Technol 4(15):1559
Spampinato C, Palazzo S, Kavasidis I (2014) A texton-based kernel density estimation approach for background modeling under extreme conditions. Comput Vision Image Understand 122:74. https://doi.org/10.1016/j.cviu.2013.12.003
Srividhya K, Ramya MM (2017) Accurate object recognition in the underwater images using learning algorithms and texture features. Multimedia Tools Appl 76(24):25679. https://doi.org/10.1007/s11042-017-4459-6
N.M.H.K.S..G.B. Srikanth Vasamsetti, Supriya Setia, IET Computer Vision (2018)
Stauffer C, Grimson WEL (1999) in Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 2, p. 252. https://doi.org/10.1109/CVPR.1999.784637
Stolkin R, Greig A, Hodgetts M, Gilby J (2008) An EM/E-MRF algorithm for adaptive model based tracking in extremely poor visibility. Image Vision Comput 26(4):480
G.D.M. Systems. Bluefin sandshark micro-auvs conduct simulated missions with a bluefin-21 uuv. https://www.youtube.com/watch?v=qIKmfOWcpzk (2016)
Szolgay D, Benois-Pineau J, Megret R, Gaestel Y, Dartigues JF (2011) Detection of moving foreground objects in videos with strong camera motion. Patt Anal Appl 14(3):311. https://doi.org/10.1007/s10044-011-0221-2
Toyama K, Krumm J, Brumitt B, Meyers B (1999) in Proceedings of the seventh IEEE international conference on computer vision, (IEEE), vol. 1, pp. 255–261
Trumpet C (2017) Giant sea turtles . amazing coral reef fish. https://www.youtube.com/watch?v=riFyKUyGb4k
Vemulapalli R, Aravind R (2009) in 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1145–1152. https://doi.org/10.1109/ICCVW.2009.5457574
Wintenby J, Svensson D (2015) in 2015 18th International Conference on Information Fusion (Fusion), pp. 1786–1793
Xu Y, Dong J, Zhang B, Xu D (2016) Background modeling methods in video analysis: A review and comparative evaluation. CAAI Trans Intell Technol 1(1):43–60. https://doi.org/10.1016/j.trit.2016.03.005
Yang Y, Liu Y (2011) in Proceedings of 2011 International Conference on Computer Science and Network Technology, vol. 2, pp. 1050–1054. https://doi.org/10.1109/ICCSNT.2011.6182141
Zamalieva D, Yilmaz A (2014) Background subtraction for the moving camera: A geometric approach. Comput Vision Image Understand 127:73
Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Patt Anal Mach Intell 22(11):1330
Zhang Y, Wang X, Qu B (2012) Procedia Engineering 29:2705
Zhao S, Chen B, Príncipe JC (2011) in The 2011 International Joint Conference on Neural Networks, pp. 2012–2017. https://doi.org/10.1109/IJCNN.2011.6033473
Zhong Z, Wen J, Zhang B, Xu Y (2019) A general moving detection method using dual-target nonparametric background model. Knowl-Based Syst 164:85
Zhou F, Cui Y, Peng B (1840) Wang Y (2012) A novel optimization method of camera parameters used for vision measurement. Optics Laser Technol 44(6):
Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, Wells WM III, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acade Radiol 11(2):178
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Susmita Panda(\(1^{st}\) author) involved in formulation and development of algorithm, validating the algorithms with different datasets and manuscript preparation. Pradipta Kumar Nanda(\(2^{nd}\) author) involved in conceptualization of the problem, problem formulation, and validating results and manuscript.
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Panda, S., Nanda, P.K. Kernel density estimation and correntropy based background modeling and camera model parameter estimation for underwater video object detection. Soft Comput 25, 10477–10496 (2021). https://doi.org/10.1007/s00500-021-05919-7
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DOI: https://doi.org/10.1007/s00500-021-05919-7