Abstract
We present a hierarchical grid-based tracking methodology for multiple people tracking in a multi-camera setup. In this system, frame-by-frame detection is performed by means of hierarchical likelihood grids, by matching shape templates through an oriented distance transform over foreground intensity edges, followed by clustering in pose-space. Subsequently, multi-target tracking is achieved by means of global nearest neighbor data association, with a fully automatic initialization, maintainance and termination strategy. We demonstrate our system through experiments in indoor sequences, using a four-camera calibrated setup. Moreover, in the paper we present the improvements obtained by means of a fast algorithm for computing the oriented DT, as well as using multi-part shape templates in place of a simple cylinder model, for a more precise localization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gavrila, D.M.: Pedestrian Detection from a Moving Vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000)
Panin, G., Lenz, C., Nair, S., Roth, E., Wojtczyk, M., Friedlhuber, T., Knoll, A.: A Unifying Software Architecture for Model-Based Visual Tracking. In: IS&T/SPIE 20th Annual Symposium of Electronic Imaging, San Jose, CA (2008)
Olsen, C.F., Huttenlocher, D.P.: Automatic Target Recognition by Matching Oriented Edge Pixels. IEEE Transactions on Image Processing 6(1), 103–113 (1997)
Andriluka, M., Roth, S., Schile, B.: People-Tracking-by-Detection and People-Detection-by-Tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)
Wu, B., Nevatia, R.: Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors. International Journal of Computer Vision 75(2), 247–266 (2007)
Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity using Real-Time Tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
Eng, H., Wang, J., Kam, A., Yau, W.: A Bayesian Framework for Robust Human Detection and Occlusion Handling using a Human Shape Model. In: International Conference on Pattern Recognition, pp. 257–260 (2004)
Okuma, K., Taleghani, A., de Freitas, N., Little, J.J., Lowe, D.G.: A Boosted Particle Filter: Multitarget Detection and Tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)
Leibe, B., Schindler, K., Van Gool, L.: Coupled Detection and Trajectory Estimation for Multi-Object Tracking. In: International Conference on Computer Vision (2007)
Panin, G.: Model-based Visual Tracking: the OpenTL Framework. Wiley-Blackwell (2011)
Bar-Shalom, Y., Li, X.: Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing (1995)
Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, San Diego (1988)
Fortmann, T.E., Bar-Shalom, Y., Scheffe, M.: Sonar Tracking of Multiple Targets using Joint Probabilistic Data Association. IEEE Journal of Oceanic Engineering 8(3), 173–184 (1983)
Reid, D.B.: An Algorithm for Tracking Multiple Targets. IEEE Transaction on Automatic Control 24(6), 843–854 (1979)
Konstantinova, P., Udvarev, A., Semerdjiev, T.: A Study of a Target Tracking Algorithm using Global Nearest Neighbor Approach. In: Proceeding of International Conference on Computer Systems and Technologies (2003)
Burgeois, F., Lasalle, J.C.: An Extension of the Munkres Algorithm for the Assignment Problem to Rectangular Matrices. Communications of the ACM, 802–806 (1971)
Fieguth, P., Terzopoulos, D.: Color-based Tracking of Heads and other Mobile Objects at Video Frame Rates. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 21–27 (1997)
Roh, M.C., Kim, T.Y., Park, J., Lee, S.W.: Accurate Object Contour Tracking based on Boundary Edge Selection. Pattern Recognition 40(3), 931–943 (2007)
Nguyen, H.T., Worring, M., Van Den Boomgaard, R., Smeulders, A.W.M.: Tracking Nonparameterized Object Contours in Video. IEEE Trans. Image Process. 11(9), 1081–1091 (2002)
Andriluka, M., Roth, S., Schiele, B.: Monocular 3D Pose Estimation and Tracking by Detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2010)
Khan, S., Javed, O., Rasheed, Z., Shah, M.: Human Tracking in Multiple Cameras. In: Proceedings of the 8th IEEE International Conference on Computer Vision, Vancouver, Canadam, pp. 331–336 (2001)
Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Foreground Object Detection from Videos Containing Complex Background. In: Proceedings of the 11th ACM International Conference on Multimedia, pp. 2–10 (2003)
Gavrila, D.M., Davis, L.S.: 3-D Model-based Tracking of Humans in Action: a Multi-View Approach. In: Proc. IEEE Computer Vision and Pattern Recognition, San Francisco, pp. 73–80 (1996)
Borgefors, G.: Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 10(6), 849–865 (1988)
Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Model-based Hand Tracking using a Hierarchical Bayesian Filter. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1372–1384 (2006)
Chen, L., Panin, G., Knoll, A.: Multi-camera People Tracking with Hierarchical Likelihood Grids. In: Proceedings of the 6th International Conference on Computer Vision Theory and Applications. INSTICC Press, Algarve (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, L., Panin, G., Knoll, A. (2013). Hierarchical Grid-Based People Tracking with Multi-camera Setup. In: Csurka, G., Kraus, M., Mestetskiy, L., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Applications. VISIGRAPP 2011. Communications in Computer and Information Science, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32350-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-32350-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32349-2
Online ISBN: 978-3-642-32350-8
eBook Packages: Computer ScienceComputer Science (R0)