Abstract
Developing a practical multi-camera tracking solution for autonomous camera networks is a very challenging task, due to numerous constraints such as limited memory and processing power, heterogeneous visual characteristics of objects between camera views, and limited setup time and installation knowledge for camera calibration. In this paper, we propose a unified multi-camera tracking framework, which can run online in real-time and can handle both independent field of view and common field of view cases. No camera calibration, knowledge of the relative positions of cameras, or entry and exit locations of objects is required. The memory footprint of the framework is minimised by the introduction of reusing kernels. The heterogeneous visual characteristics of objects are addressed by a novel location-based kernel matching method. The proposed framework has been evaluated using real videos captured in multiple indoor settings. The framework achieves efficient memory usage without compromising tracking accuracy.
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
Arsic, D., Lyutskanov, A., Rigoll, G., Kwolek, B.: Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework. In: 12th IEEE Int Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–8. IEEE, Los Alamitos (2009)
Khan, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1355–1360 (2003)
Lee, L., Romano, R., Stein, G.: Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Trans. Pattern Anal. Mach. Intell. 22, 758–767 (2000)
Makris, D., Ellis, T., Black, J.: Bridging the gaps between cameras. In: Int Conf Computer Vision Pattern Recognition (CVPR), pp. 205–210. IEEE, Los Alamitos (2004)
Cichowski, A., Madden, C., Detmold, H., Dick, A., van den Hengel, A., Hill, R.: Tracking hand-off in large surveillance networks. In: 24th Int Conf on Image and Vision Computing New Zealand (IVCNZ), pp. 276–281. IEEE, Los Alamitos (2009)
Sheikh, Y., Li, X., Shah, M.: Trajectory Association across Non-overlapping Moving Cameras in Planar Scenes. In: Int Conf Computer Vision Pattern Recognition (CVPR), pp. 1–7. IEEE, Los Alamitos (2007)
Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. In: 9th IEEE Int Conf on Computer Vision (ICCV), pp. 952–957. IEEE, Los Alamitos (2003)
Petrushin, V.A., Wei, G., Gershman, A.V.: Multiple-camera people localization in an indoor environment. Knowl Inf. Syst. 10, 229–241 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wedge, D., Scott, A.F., Ma, Z., Vendrig, J. (2010). Object Tracking over Multiple Uncalibrated Cameras Using Visual, Spatial and Temporal Similarities. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-17691-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17690-6
Online ISBN: 978-3-642-17691-3
eBook Packages: Computer ScienceComputer Science (R0)