Abstract
In Intelligent Transportation Systems (ITS’s), the process of vehicle tracking is often needed to permit higher-level analysis. However, during occlusions features could “jump” from an object to another one, thus resulting in tracking errors. Our method exploits second order statistics to assess the correct membership of features to respective objects, thus reducing false alarms due to splitting. As a consequence, object’s properties like area and centroid can be extracted stemming from feature points with a higher precision. We firstly validated our method on toy sequences built ad hoc to produce strong occlusions artificially and subsequently on sequences taken from a traffic monitoring system. The experimental results we present prove the effectiveness of our approach even in the presence of strong occlusions. At present, the algorithm is working in the daytime in the Traffic Monitoring System of the city where we have been living.
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
Beymer, D., McLauchlan, P., Coifman, B., Malik, J.: A Real-Time Computer Vision System for Measuring Traffic Parameters. In: Proc. IEEE CVPR, San Juan, Puerto Rico, pp. 495–501 (1997)
Sugaya, Y., Kanatani, K.: Outlier Removal for Motion Tracking by Subspace Separation. In: 8th Symposium on Sensing via Image Information (SSII 2002), Yokohama, Japan, pp. 603–608 (July 2002)
Nguyen, H.T., Smeulders, A.W.M.: Fast Occluded Object Tracking by a Robust Appearance Filter. IEEE Transactions on PAMI, 1099–1104 (August 2004)
Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: Proc. IEEE ICCV, pp. 975–982 (1999)
Bevilacqua, A., Di Stefano, L., Vaccari, S.: Occlusion Robust Vehicle Tracking based on SOM (Self-Organizing Map). In: 7th IEEE Workshop on Motion and Video Computing (Motion 2005), Breckenridge, CO, USA, October 5-7, pp. 84–89 (2005)
Bevilacqua, A., Di Stefano, L., Lanza, A.: An efficient motion detection algorithm based on a statistical non parametric noise model. In: 17th IEEE International Conference on Image Processing (ICIP 2004), Singapore, October 24-27, pp. 2347–2350 (2004)
Bevilacqua, A., Di Stefano, L., Lanza, A.: A Simple Self-Calibration Method To Infer A Non-Parametric Model Of The Imaging System Noise. In: 7th IEEE Workshop on Motion and Video Computing (Motion 2005), Breckenridge, CO, USA, January 5-7, vol. 2, pp. 229–234 (2005)
Xiao, J., Shah, M.: Motion Layer Extraction in the Presence of Occlusion Using Graph Cut. In: Proc. IEEE CVPR, Washington, DC, USA, vol. 2, pp. 972–979 (2004)
Shi, J., Tomasi, C.: Good features to track. In: Proc. IEEE CVPR, pp. 593–600 (1994)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: DARPA Image Understanding Workshop, pp. 121–130 (1981)
Birchfield, S.: Derivation of Kanade-Lucas-Tomasi tracking equation (unpublished, January 1997)
Baker, S., Matthews, I.: Lucas-Kanade 20 Years On: A Unifying Framework. IJCV 56(3), 221–255 (2004)
Chang, P., Krumm, J.: Object Recognition with Color Cooccurrence Histograms. In: Proc. IEEE CVPR, Fort Collins, CO, pp. 498–504 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bevilacqua, A., Vaccari, S. (2006). A Novel Spatio-temporal Approach to Handle Occlusions in Vehicle Tracking. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_82
Download citation
DOI: https://doi.org/10.1007/11867586_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44891-4
Online ISBN: 978-3-540-44893-8
eBook Packages: Computer ScienceComputer Science (R0)