Abstract
We propose a real time algorithm to track moving persons without any a priori knowledge neither on the model of person, nor on their size or their number, which can evolve with time. It manages several problems such as occlusion and under or over-segmentations. The first step consisting in motion detection, leads to regions that have to be assigned to trajectories. This tracking step is achieved using a new concept: elementary tracks. They allow on the one hand to manage the tracking and on the other hand, to detect the output of occlusion by introducing coherent sets of regions. Those sets enable to define temporal kinematical model, shape model or colour model. Significant results have been obtained on several sequences with ground truth as shown in results.
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
Preview
Unable to display preview. Download preview PDF.
References
Achard, C., Mostafaoui, G., Milgram, M.: Object tracking based on kinematics with spatio-temporal blob. To appear in MVA 2005 (2005)
Bar-Shalom, Y., Li, X.R.: Multitarget-Mulisensor tracking. Yaakov Bar-Shalom (1995)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition using silhouettes, International Conference on Pattern Recognition, June 2000, pp. 77–82 (1998)
Denoulet, J., Mostafaoui, G., Lacassagne, L., Merigot, A.: Robust Embedded Hardware implementation of Motion Markov Detection and hysteresis thresholding in colors sequences, pp. 142–151 (to appear)
Haritaoglu, I., Harwood, D., Davis, L.S.: Ghost: A human body part labelling system. In: CAMP 2005 (2005)
Haritaoglu, I., Harwood, D., Davis, L.S.: W4S: a real time system for detecting and tracking people in 2,5D. In: European Conference Computer Vision, Maryland, pp. 877–892 (1998)
Hue, C., Le, J.P.: cadre, P. Perez, Tracking multiple objects with particle filtering, RR INRIA no 4033 (2000)
Isard, M., Blake, A.: Condensation conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)
Moon, H., Chellappa, R., Rosenfeld, A.: Tracking of Human Activities Using Shape-encoded Particle Propagation. In: ICIP 2001, vol. 1, pp. 357–360 (2001)
Mittal, A., Davis, L.S.: M2 Tracker: A Multi-View Approach to Segmenting and tracking people in a Cluttered Scene. IJCV(51) (3), 189–203 (2003)
Park, S., Aggarwal, J.K.: Segmentation and tracking of interacting human body parts under occlusion and shadowing. In: Motion 2002, pp. 105–111 (2002)
Reid, D.B.: An algorithm for Tracking Multiple Targets. IEEE Trans. on Automatic Control AC-24(6), 843–854 (1979)
Senior, A.: Tracking People with Probabilistic Appearance Models. In: Pets 2002, pp. 48–55 (2002)
Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. In: ICCV 2003 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mostafoui, G., Achard, C., Milgram, M. (2005). Real Time Tracking of Multiple Persons on Colour Image Sequences. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_6
Download citation
DOI: https://doi.org/10.1007/11558484_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
eBook Packages: Computer ScienceComputer Science (R0)