Abstract
Object detection is an important and challenging task in computer vision. In cascaded detectors, a scanned image is passed through a cascade in which all stage detectors have to classify a found object positively. Common detection algorithms use a sliding window approach, resulting in multiple detections of an object. Thus, the merging of multiple detections is a crucial step in post-processing which has a high impact on the final detection performance. First, this paper proposes a novel method for merging multiple detections that exploits intra-cascade confidences using Dempster’s Theory of Evidence. The evidence theory allows hereby to model confidence and uncertainty information to compute the overall confidence measure for a detection. Second, this confidence measure is applied to improve the accuracy of the determined object position. The proposed method is evaluated on public object detection benchmarks and is shown to improve the detection performance.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156 (1996)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2) (2010)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)
Huang, C., Al, H., Wu, B., Lao, S.: Boosting nested cascade detector for multi-view face detection. In: Pattern Recognition, ICPR 2004 (2004)
Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological) 30(2), 205–247 (1968)
Shafer, G.: A mathematical theory of evidence, vol. 1. Princeton University Press, Princeton (1976)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Jain, V., Learned-Miller, E.: Fddb: A benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, University of Massachusetts, Amherst (2010)
Sung, K.K., Poggio, T., Rowley, H.A., Baluja, S., Kanade, T.: MIT+CMU frontal face dataset a, b and c. MIT+CMU (1998)
Agarwal, S., Awan, A., Roth, D.: UIUC image database for car detection (2002)
TheMPLab GENKI Database, u.S., http://mplab.ucsd.edu
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
Ehlers, A., Scheuermann, B., Baumann, F., Rosenhahn, B. (2013). Cleaning Up Multiple Detections Caused by Sliding Window Based Object Detectors. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-41822-8_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
eBook Packages: Computer ScienceComputer Science (R0)