Abstract
Tracking multiple objects into a scene is one of the most active research topics in computer vision. The art of identifying each target within the scene along a video sequence has multiple issues to be solved, being collision and occlusion events among the most challenging ones. Because of this, when dealing with human detection, it is often very difficult to obtain a full body image, which introduces complexity in the process. The task becomes even more difficult when dealing with unpredictable trajectories, like in sport environments. Thus, head-shoulder omega shape becomes a powerful tool to perform the human detection. Most of the contributions to this field involve a detection technique followed by a tracking system based on the omega-shape features. Based on these works, we present a novel methodology for providing a full tracking system. Different techniques are combined to both detect, track and recover target identifications under unpredictable trajectories, such as sport events. Experimental results into challenging sport scenes show the performance and accuracy of this technique. Also, the system speed opens the door for obtaining a real-time system using GPU programing in standard desktop machines, being able to be used in higher-level human behavioral systems, with multiple applications.
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Andriyenko, A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1265–1272. IEEE, New York (2011)
Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1926–1933. IEEE, New York (2012)
Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking. IEEE Trans Signal Process 50(2), 174–188 (2002)
Benfold, B., Reid, I.: Stable multi-target tracking in real-time surveillance video. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3457–3464. IEEE, New York (2011)
Cancela, B., Ortega, M., Fernández, A., Penedo, M.G.: Hierarchical framework for robust and fast multiple-target tracking. Expert Syst. Appl. 40, 1116–1131 (2013)
Cancela, B., Ortega, M., Penedo, M.G.: Human detection and tracking under complex activities. In: 8th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2012) (2013)
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. PAMI 27(10), 1631–1643 (2005)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000, vol. 2, pp. 142–149 (2000)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), vol. 1, pp. 886–893 (2005)
Desai, C., Ramanan, D., Fowlkes, C.: Discriminative models for multi-class object layout. In: IEEE 12th International Conference on Computer Vision, 2009, pp. 229–236 (2009)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Haritaoglu, I., Harwood, D., Davis, L.: W4: Who? when? where? what? a real time system for detecting and tracking people. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 222–227 (1998)
Hoiem, D., Efros, A., Hebert, M.: Putting objects in perspective. Int. J. Comput. Vis. 80, 3–15 (2008)
Kalman, R.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. (Ser. D) 82, 35–45 (1960)
Leal-Taixé, L., Pons-Moll, G., Rosenhahn, B.: Everybody needs somebody: modeling social and grouping behavior on a linear programming multiple people tracker. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 120–127. IEEE, New York (2011)
Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: 19th International Conference on Pattern Recognition, 2008 (ICPR 2008), pp. 1–4 (2008)
Li, M., Zhang, Z., Huang, K., Tan, T.: Rapid and robust human detection and tracking based on omega-shape features. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 2545–2548 (2009)
Marieb, E.N., Hoehn, K.: Human Anatomy and Physiology. Pearson Education, San Francisco (2007)
Porikli, F.: Integral histogram: a fast way to extract histograms in cartesian spaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), vol. 1, pp. 829–836 (2005)
Rodriguez, M., Sivic, J., Laptev, I., Audibert, J.Y.: Density-aware person detection and tracking in crowds. In: Proceedings of the International Conference on Computer Vision (ICCV) (2011)
Rohr, K.: Towars model-based recognition of human movements in image sequences. CVGIP Image Underst. 59(1), 94–115 (1994)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994 (CVPR ’94), pp. 593–600 (1994)
Stalder, S., Grabner, H., Van Gool, L.: Cascaded confidence filtering for improved tracking-by-detection. In: Computer Vision-ECCV 2010, pp. 369–382. Springer, Berlin (2010)
Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J., Mostefa, D., Soundararajan, P.: The clear 2006 evaluation. In: Multimodal Technologies for Perception of Humans, pp. 1–44. Springer, Berlin (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001 (CVPR 2001), vol. 1, pp. I-511–I-518 (2001)
Yang, B., Nevatia, R.: An online learned crf model for multi-target tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2034–2041. IEEE, New York (2012)
Yang, B., Nevatia, R.: Online learned discriminative part-based appearance models for multi-human tracking. In: Computer Vision-ECCV 2012, pp. 484–498. Springer, Berlin (2012)
Zhan, B., Monekosso, D., Remagnino, P., Velastin, S., Xu, L.Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19, 345–357 (2008)
Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004 (ICPR 2004), vol. 2, pp. 28–31 (2004)
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This paper has been partly funded by the Ministerio de Ciencia e Innovación through grant contract TIN2011-25476.
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Cancela, B., Ortega, M. & Penedo, M.G. Multiple human tracking system for unpredictable trajectories. Machine Vision and Applications 25, 511–527 (2014). https://doi.org/10.1007/s00138-013-0544-7
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DOI: https://doi.org/10.1007/s00138-013-0544-7