Abstract
In this paper, the difficult task of detecting fishes in underwater scenarios is analyzed with a special focus on crowded scenes where the differentiation between separate fishes is even more challenging. An extension for the Gaussian Switch Model is developed for the detection which applies an intelligent update scheme to create more accurate background models even for difficult scenes. To deal with very crowded areas in the scene we use the Flux Tensor to create a first coarse segmentation and only update areas that are with high certainty background. The spatial coherency is increased by the N\(^2\)Cut, which is a Ncut adaption to change detection. More relevant information are gathered with a novel blob tracker that uses a specially developed energy function and handling of errors during the change detection. This method keeps the generality of the whole approach so that it can be used for any moving object. The proposed algorithm enabled us to get very accurate underwater segmentations as well as precise results in tracking scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This research has been supported by the German Federal State of Mecklenburg-Western Pomerania and the European Social Fund under grant ESF/IV-BMB35-0006/12.
- 2.
- 3.
References
Radolko, M., Gutzeit, E.: Video segmentation via a Gaussian switch background-model and higher order Markov random fields. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, vol. 1, pp. 537–544 (2015)
Shelley, A.J., Seed, N.L.: Approaches to static background identification and removal. In: IEE Colloquium on Image Processing for Transport Applications, pp. 6/1–6/4 (1993)
Gardos, T., Monaco, J.: Encoding video images using foreground/background segmentation. US Patent 5,915,044 (1999)
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19, 780–785 (1997)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Schindler, K., Wang, H.: Smooth foreground-background segmentation for video processing. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 581–590. Springer, Heidelberg (2006). https://doi.org/10.1007/11612704_58
Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split Gaussian models. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 420–424 (2014)
Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20, 1709–1724 (2011)
Bianco, S., Ciocca, G., Schettini, R.: How far can you get by combining change detection algorithms? CoRR abs/1505.02921 (2015)
Xiao, Q., Liu, X., Liu, M.: Object tracking based on local feature matching. In: 2012 Fifth International Symposium on Computational Intelligence and Design, vol. 1, pp. 399–402 (2012)
Camplani, M., et al.: Multiple human tracking in RGB-depth data: a survey. IET Comput. Vis. 11, 265–285 (2016)
Shu, G., Dehghan, A., Oreifej, O., Hand, E., Shah, M.: Part-based multiple-person tracking with partial occlusion handling. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1815–1821 (2012)
Radolko, M., Farhadifard, F., von Lukas, U.F.: Change detection in crowded underwater scenes - via an extended Gaussian switch model combined with a flux tensor pre-segmentation. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), VISAPP, vol. 4, pp. 405–415 (2017)
Bunyak, F., Palaniappan, K., Nath, S.K., Seetharaman, G.: Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking. J. Multimedia 2, 20–33 (2007)
Radolko, M., Farhadifard, F., Gutzeit, E., von Lukas, U.F.: Real time video segmentation optimization with a modified normalized cut. In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 31–36 (2015)
Wang, B., Gao, Y.: Structure integral transform versus radon transform: a 2D mathematical tool for invariant shape recognition. IEEE Trans. Image Process. 25, 5635–5648 (2016)
Abukhait, J., Abdel-Qader, I., Oh, J.S., Abudayyeh, O.: Road sign detection and shape recognition invariant to sign defects. In: 2012 IEEE International Conference on Electro/Information Technology, pp. 1–6 (2012)
Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955)
Luetteke, F., Zhang, X., Franke, J.: Implementation of the Hungarian method for object tracking on a camera monitored transportation system. In: 7th German Conference on Robotics, ROBOTIK 2012, pp. 1–6 (2012)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: 17th International Conference on Proceedings of the Pattern Recognition, ICPR 2004, vol. 2, pp. 28–31 (2004)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S. (eds.) Video-Based Surveillance Systems, pp. 135–144. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-0913-4_11
Zivkovic, Z., Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27, 773–780 (2006)
Radolko, M., Farhadifard, F., von Lukas, U.F.: Dataset on underwater change detection. In: OCEANS 2016 - MONTEREY, pp. 1–8 (2016)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. 1–10
Ferryman, J., Shahrokni, A.: Pets 2009: dataset and challenge. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 1–6 (2009)
Jiang, X., Rodner, E., Denzler, J.: Multi-person tracking-by-detection based on calibrated multi-camera systems. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 743–751. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33564-8_89
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1820–1833 (2011)
Yang, J., Vela, P.A., Shi, Z., Teizer, J.: Probabilistic multiple people tracking through complex situations. In: Performance Evaluation of Tracking and Surveillance workshop at CVPR 2009, Miami, Florida, pp. 79–86 (2009)
Berclaz, J., Fleuret, F., Fua, P.: Robust people tracking with global trajectory optimization. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1, pp. 744–750 (2006)
Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: CVPR 2011, pp. 1265–1272 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Radolko, M., Farhadifard, F., von Lukas, U. (2019). Change Detection and Blob Tracking of Fish in Underwater Scenarios. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics – Theory and Applications. VISIGRAPP 2017. Communications in Computer and Information Science, vol 983. Springer, Cham. https://doi.org/10.1007/978-3-030-12209-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-12209-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12208-9
Online ISBN: 978-3-030-12209-6
eBook Packages: Computer ScienceComputer Science (R0)