Abstract
A vision-based system that can locate individual swimmers and recognize the activities is applicable for swimming gait analysis, drowning event detection, etc. The system relies on accurate detection of swimmer’s body parts such as head and upper limbs. The swimmer detection problem can be regarded as background subtraction. Swimmer detection in the aquatic environment is very difficult due to a dynamic background with water ripples, splashes, specular reflections, etc. This paper presents a swimmer detection method which utilizes both local motion and intensity information estimated from the image sequence. Local motion information is obtained by computing dense optical flow and periodogram. We adopt a heuristic approach to generate a motion map characterizing the local motions (random/stationary, ripple or swimming) of image pixels over a short duration. Intensity information is modeled as a mixture of Gaussians. Finally, using the motion map and the Gaussian models, swimmers are detected in each video frame. We test the method on video sequences captured at daytime, and nighttime, and of different swimming styles (breaststroke, freestyle, backstroke). Our method can detect swimmers much better than that using intensity information alone. In addition, we compare our method with existing algorithms—codebook model and self-organizing artificial neural networks. The methods are tested on publicly available video sequence and our swimming video sequence. We show through the quantitative measures the superiority of our method.
Similar content being viewed by others
References
Hsieh J.-W., Hsu Y.-T., Liao H.-Y.M., Chen C.-C.: Video-based human movement analysis and its application to surveillance systems. IEEE Trans. Multimed. 10(3), 372–384 (2008)
Cunado D., Nixon M.S., Carter J.N.: Automatic extraction and description of human gait models for recognition purposes. Comput. Vis. Image Underst. 90, 1–41 (2003)
Lu C.M., Ferrier N.J.: Repetitive motion analysis: segmentation and event classification. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 258–263 (2004)
Pogalin E.A., Thean H.C., Baan J., Schipper N.W., Smeulders A.W.M.: Video-based training registration for swimmers. Int. J. Comput. Sci. Sport 6, 4–17 (2007)
Lavest, J.M., Guichard, F., Rousseau, C.: Multi-view reconstruction combining underwater and air sensors. In: Proceedings of IEEE International Conference on Image Processing III, pp. 813–816 (2002)
Pia, F.: Reflections on lifeguarding surveillance programs. In: Proceedings of Reflections on Lifeguarding Conference (1994)
Ning H., Tan T., Wang L., Hu W.: Kinematics-based tracking of human walking in monocular video sequences. Image Vis. Comput. 22, 429–441 (2004)
Li L., Huang W., Gu I.Y.-H., Tian Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans. Image Process. 13(11), 1459–1472 (2004)
Stauffer C., Grimson W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)
Piccardi, M.: Background subtraction techniques: a review. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 3099–3104 (2004)
Elhabian S.Y., El-Sayed K.M., Ahmed S.H.: Moving object detection in spatial domain using background removal techniques—state-of-art. Recent Pat. Comput. Sci. 1(1), 32–54 (2008)
Bouwmans T., El Baf F., Vachon B.: Background modeling using mixture of Gaussians for foreground detection—a survey. Recent Pat. Comput. Sci. 1(3), 219–237 (2008)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Signal Process. 2010, (2010)
Zha Y., Bi D., Yang Y.: Learning complex background by multi-scale discriminative model. Pattern Recognit. Lett. 30, 1003–1014 (2009)
Liao, S., Zhao, G., Kellokumpu, V., Pietikäinen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301–1306 (2010)
Ko, T., Soatto, S., Estrin, D.: Warping background subtraction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1331–1338 (2010)
El Baf, F., Bouwmans, T., Vachon, B.: Type-2 fuzzy mixture of Gaussians model: application to background modeling. In: Proceedings of International Symposium on Visual Computing LNCS, vol. 5358, Part I, pp. 772–781 (2008)
Lu W., Tan Y.P.: A vision-based approach to early detection of drowning incidents in swimming pools. IEEE Trans. Circuits Syst. Video Technol. 14(2), 159–178 (2004)
Kam, A.H., Lu, W., Yau, W.-Y.: A video-based drowning detection system. In: Proceedings of European Conference on Computer Vision LNCS, vol. 2353, pp. 297–311 (2002)
Eng, H.-L., Toh, K.-A., Kam, A.H., Wang, J., Yau, W.-Y.: An automatic drowning detection surveillance system for challenging outdoor pool environments. In: Proceedings of IEEE International Conference on Computer Vision (2003)
Eng, H.-L., Wang, J., Kam, A.H., Yau, W.-Y.: Novel region-based modeling for human detection within highly dynamic aquatic environment. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2004)
Eng H.-L., Toh K.-A., Yau W.-Y., Wang J.: DEWS: a live visual surveillance system for early drowning detection at pool. IEEE Trans. Circuits Syst. Video Technol. 18(2), 196–210 (2008)
Eng H.-L., Wang J., Kam A.H., Yau W.-Y.: Robust human detection within a highly dynamic aquatic environment in real time. IEEE Trans. Image Process. 15(6), 1583–1600 (2006)
Wang, J., Eng, H.-L., Kam, A.H., Yau, W.-Y.: Integrating color and motion to enhance human detection within aquatic environment. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1179–1182 (2004)
Zhou, D., Zhang, H.: Modified GMM background modeling and optical flow for detection of moving objects. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 2224–2229 (2005)
Tang, P., Gao, L., Liu, Z.: Salient moving object detection using stochastic approach filtering. In: Proceedings of International Conference on Image and Graphics, pp. 530–535 (2007)
Sakaino, H.: Fluid motion estimation method based on physical properties of waves. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Li, F., Xu, L., Guyenne, P., Yu, J.: Recovering fluid-type motions using Navier–Stokes potential flow. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2448–2455 (2010)
Kim K., Chalidabhongse T.H., Harwood D., Davis L.S.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)
Maddalena L., Petrosino A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. Image Process. 17(7), 1168–1177 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chan, K.L. Detection of swimmer using dense optical flow motion map and intensity information. Machine Vision and Applications 24, 75–101 (2013). https://doi.org/10.1007/s00138-012-0419-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-012-0419-3