Abstract
This paper addresses the problem of detecting and tracking a large number of individuals in aerial image sequences that have been taken from high altitude. We propose a method which can handle the numerous challenges that are associated with this task and demonstrate its quality on several test sequences. Moreover this paper contains several contributions to improve object detection and tracking in other domains, too. We show how to build an effective object detector in a flexible way which incorporates the shadow of an object and enhanced features for shape and color. Furthermore the performance of the detector is boosted by an improved way to collect background samples for the classifier training. At last we describe a tracking-by-detection method that can handle frequent misses and a very large number of similar objects.
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
Baumann, A., Boltz, M., Ebling, J., Koenig, M., Loos, H.S., Merkel, M., Niem, W., Warzelhan, J.K., Yu, J.: A review and comparison of measures for automatic video surveillance systems. EURASIP Journal on Image and Video Processing, 30 (2008)
Benedek, C., Sziranyi, T., Kato, Z., Zerubia, J.: Detection of object motion regions in aerial image pairs with a multilayer markovian model. IEEE Transactions on Image Processing 18(10), 2303–2315 (2009)
Betke, M., Hirsh, D.E., Bagchi, A., Hristov, N.I., Makris, N.C., Kunz, T.H.: Tracking large variable numbers of objects in clutter. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Burkert, F., Schmidt, F., Butenuth, M., Hinz, S.: People tracking and trajectory interpretation in aerial image sequences. In: Photogrammetric Computer Vision and Image Analysis. IAPRS, vol. XXXVIII, Part 3A, pp. 209–214 (September 2010)
Collins, J., Uhlmann, J.: Efficient gating in data association with multivariate gaussian distributed states. IEEE Transactions on Aerospace and Electronic Systems 28(3), 909–916 (1992)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28(2), 337–374 (2000)
Grabner, H., Nguyen, T.T., Gruber, B., Bischof, H.: On-line boosting-based car detection from aerial images. ISPRS Journal of Photogrammetry and Remote Sensing 63(3), 382–396 (2008)
Huang, C., Wu, B., Nevatia, R.: Robust object tracking by hierarchical association of detection responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)
Kumar, R., Sawhney, H., Samarasekera, S., Hsu, S., Tao, H., Guo, Y., Hanna, K., Pope, A., Wildes, R., Hirvonen, D., Hansen, M., Burt, P.: Aerial video surveillance and exploitation. Proceedings of the IEEE 89(10), 1518–1539 (2001)
Leitloff, J., Hinz, S., Stilla, U.: Vehicle detection in very high resolution satellite images of city areas. IEEE Transactions on Geoscience and Remote Sensing 48(7), 2795–2806 (2010)
Medioni, G., Cohen, I., Bremond, F., Hongeng, S., Nevatia, R.: Event detection and analysis from video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(8), 873–889 (2001)
Smal, I., Loog, M., Niessen, W., Meijering, E.: Quantitative comparison of spot detection methods in fluorescence microscopy. IEEE Transactions on Medical Imaging 29(2), 282–301 (2010)
Ohta, Y.I., Kanade, T., Sakai, T.: Color information for region segmentation. Computer Graphics and Image Processing 13(3), 222–241 (1980)
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 193–199 (1997)
Reilly, V., Idrees, H., Shah, M.: Detection and tracking of large number of targets in wide area surveillance. In: Daniilidis, K. (ed.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 186–199. Springer, Heidelberg (2010a)
Reilly, V., Solmaz, B., Shah, M.: Geometric constraints for human detection in aerial imagery. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 252–265. Springer, Heidelberg (2010)
Smal, I., Loog, M., Niessen, W., Meijering, E.: Quantitative comparison of spot detection methods in fluorescence microscopy. IEEE Transactions on Medical Imaging 29(2), 282–301 (2010)
Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 39–51 (1998)
Thomas, U., Rosenbaum, D., Kurz, F., Suri, S., Reinartz, P.: A new software/hardware architecture for real time image processing of wide area airborne camera images. Journal of Real-Time Image Processing 4(3), 229–244 (2008)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
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, 247–266 (2007)
Xiao, J., Yang, C., Han, F., Cheng, H.: Vehicle and person tracking in aerial videos. In: Stiefelhagen, R., Bowers, R., Fiscus, J.G. (eds.) RT 2007 and CLEAR 2007. LNCS, vol. 4625, pp. 203–214. Springer, Heidelberg (2008)
Yu, Q., Cohen, I., Medioni, G., Wu, B.: Boosted markov chain monte carlo data association for multiple target detection and tracking. In: International Conference on Pattern Recognition, vol. 2, pp. 675–678 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schmidt, F., Hinz, S. (2011). A Scheme for the Detection and Tracking of People Tuned for Aerial Image Sequences. In: Stilla, U., Rottensteiner, F., Mayer, H., Jutzi, B., Butenuth, M. (eds) Photogrammetric Image Analysis. PIA 2011. Lecture Notes in Computer Science, vol 6952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24393-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-24393-6_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24392-9
Online ISBN: 978-3-642-24393-6
eBook Packages: Computer ScienceComputer Science (R0)