Abstract
Generalized Hough Transform-based methods have been successfully applied to object detection. Such methods have the following disadvantages: (i) manual labeling of training data ; (ii) the off-line construction of codebook. To overcome these limitations, we propose an unsupervised moving object detection algorithm with on-line Generalized Hough Transform. Our contributions are two-fold: (i) an unsupervised training data selection algorithm based on Multiple Instance Learning (MIL); (ii) an on-line Extremely Randomized Trees construction algorithm for on-line codebook adaptation. We evaluate the proposed algorithm on three video datasets. The experimental results show that the proposed algorithm achieves comparable performance to the supervised detection method with manual labeling. They also show that the proposed algorithm outperforms the previously proposed unsupervised learning algorithm.
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
Dalai, N., Triggs, B., Rhone-Alps, I., Montbonnot, F.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1 (2005)
Munder, S., Gavrila, D.: An experimental study on pedestrian classification. TPAMI (2006)
Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)
Fergus, R., Perona, P., Zisserman, A.: Weakly supervised scale-invariant learning of models for visual recognition. IJCV (2007)
Leibe, B., Leonardis, A., Schiele, B.: Robust Object Detection with Interleaved Categorization and Segmentation. IJCV (2008)
Okada, R.: Discriminative Generalized Hough Transform for Object Detection. In: ICCV (2009)
Balcan, M., Blum, A., Yang, K.: Co-training and expansion: Towards bridging theory and practice. In: NIPS (2005)
Javed, O., Ali, S., Shah, M.: Online detection and classification of moving objects using progressively improving detectors. In: CVPR (2005)
Wu, B., Nevatia, R.: Improving part based object detection by unsupervised, online boosting. In: CVPR (2007)
Nair, V., Clark, J.: An unsupervised, online learning framework for moving object detection. In: CVPR (2004)
Roth, P., Grabner, H., Skocaj, D., Bischof, H., Leonardis, A.: On-line conservative learning for person detection. In: VS-PETS (2005)
Gall, J., Lempitsky, V.: Class-Specific Hough Forests for Object Detection. In: CVPR (2009)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line Random Forests. In: The 3rd On-line Learning for Computer Vision Workshop (2009)
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: NIPS (1998)
Li, W., Yeung, D.: Localized content-based image retrieval through evidence region identification. In: CVPR (2009)
Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: ICCV (2009)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning (2006)
Canny, J.: A computational approach to edge detection. TPAMI (1986)
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
Xu, J., Wang, Y., Wang, W., Yang, J., Li, Z. (2011). Unsupervised Moving Object Detection with On-line Generalized Hough Transform. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-19318-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19317-0
Online ISBN: 978-3-642-19318-7
eBook Packages: Computer ScienceComputer Science (R0)