Abstract
We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous work, this is accomplished via the generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locations of the centroid of the whole object; the detection hypotheses then correspond to the maxima of the Hough image that accumulates the votes from all parts. However, whereas previous methods detect object parts using generative codebooks of part appearances, we take a more discriminative approach to object part detection. Towards this end, we train a class-specific Hough forest, which is a decision forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough-transform object detection significantly and achieve state-of-the-art performance for several classes and datasets. Parts of this chapter are reprinted, with permission, from Gall and Lempitsky, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVRP) (2009), © 2012 IEEE.
This chapter is based on the CVPR’09 conference paper [118].
Parts of this chapter are reprinted, with permission, from [118], © 2012 IEEE.
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
References
Agarwal S, Awan A, Roth D (2004) Learning to detect objects in images via a sparse, part-based representation. IEEE Trans Pattern Anal Mach Intell 26(11)
Amit Y, Geman D, Wilder K (1997) Joint induction of shape features and tree classifiers. IEEE Trans Pattern Anal Mach Intell 19
Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 13(2)
Barinova O, Lempitsky VS, Kohli P (2010) On detection of multiple object instances using Hough transforms. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Barinova O, Lempitsky VS, Kohli P (2012) On detection of multiple object instances using Hough transforms. IEEE Trans Pattern Anal Mach Intell
Borenstein E, Ullman S (2002) Class-specific, top-down segmentation. In: Proc European conf on computer vision (ECCV). LNCS, vol 2351. Springer, Berlin
Breiman L (2001) Random forests. Mach Learn 45(1)
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proc IEEE conf computer vision and pattern recognition (CVPR), June 2005, vol 2
Fanelli G, Gall J (2011) Real time head pose estimation with random regression forests. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Gall J, Lempitsky VS (2009) Class-specific Hough forests for object detection. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Gall J, Yao A, Razavi N, van Gool LJ, Lempitsky VS (2011) Hough forests for object detection, tracking, and action recognition. IEEE Trans Pattern Anal Mach Intell 33(11)
Girshick R, Shotton J, Kohli P, Criminisi A, Fitzgibbon A (2011) Efficient regression of general-activity human poses from depth images. In: Proc IEEE intl conf on computer vision (ICCV)
Glocker B, Pauly O, Konukoglu E, Criminisi A (2012) Joint classification-regression forests for spatially structured multi-object segmentation. In: Proc European conf on computer vision (ECCV). Springer, Berlin
Godec M, Roth PM, Bischof H (2011) Hough-based tracking of non-rigid objects. In: Proc IEEE intl conf on computer vision (ICCV)
Lampert C, Blaschko M, Hofmann T (2008) Beyond sliding windows: object localization by efficient subwindow search. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Leibe B, Schiele B (2003) Interleaved object categorization and segmentation. In: Proc British machine vision conference (BMVC), vol II
Leibe B, Leonardis A, Schiele B (2008) Robust object detection with interleaved categorization and segmentation. Int J Comput Vis 77(1–3)
Lepetit V, Lagger P, Fua P (2005) Randomized trees for real-time keypoint recognition. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Marée R, Geurts P, Piater J, Wehenkel L (2005) Random subwindows for robust image classification. In: Proc IEEE conf computer vision and pattern recognition (CVPR), vol 1. IEEE, New York
Moosmann F, Triggs B, Jurie F (2006) Fast discriminative visual codebooks using randomized clustering forests. In: Advances in neural information processing systems (NIPS)
Mutch J, Lowe DG (2006) Multiclass object recognition with sparse, localized features. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag-of-features image classification. In: Proc European conf on computer vision (ECCV). Springer, Berlin
Okada R (2009) Discriminative generalized Hough transform for object detection. In: Proc IEEE intl conf on computer vision (ICCV)
Opelt A, Pinz A, Zisserman A (2008) Learning an alphabet of shape and appearance for multi-class object detection. Int J Comput Vis
Pang J, Huang Q, Jiang S (2008) Multiple instance boost using graph embedding based decision stump for pedestrian detection. In: Proc European conf on computer vision (ECCV). Springer, Berlin
Razavi N, Gall J, Van Gool L (2010) Backprojection revisited: scalable multi-view object detection and similarity metrics for detections. In: Proc European conf on computer vision (ECCV). Springer, Berlin
Schroff F, Criminisi A, Zisserman A (2008) Object class segmentation using random forests. In: Proc British machine vision conference (BMVC)
Seemann E, Schiele B (2006) Cross-articulation learning for robust detection of pedestrians. In: Proc annual symposium of the German association for pattern recognition (DAGM)
Sharp T (2008) Implementing decision trees and forests on a GPU. In: Proc European conf on computer vision (ECCV). Springer, Berlin
Shotton J, Blake A, Cipolla R (2008) Efficiently combining contour and texture cues for object recognition. In: Proc British machine vision conference (BMVC)
Shotton J, Blake A, Cipolla R (2008) Multiscale categorical object recognition using contour fragments. IEEE Trans Pattern Anal Mach Intell 30(7)
Shotton J, Johnson M, Cipolla R (2008) Semantic texton forests for image categorization and segmentation. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Torralba A, Murphy KP, Freeman WT, Rubin MA (2003) Context-based vision system for place and object recognition. In: Proc IEEE intl conf on computer vision (ICCV), Nice, France, October 2003, vol 2
Winn J, Shotton J (2006) The layout consistent random field for recognizing and segmenting partially occluded objects. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Yao A, Gall J, van Gool L (2010) A Hough transform-based voting framework for action recognition. In: Proc IEEE conf computer vision and pattern recognition (CVPR)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Gall, J., Lempitsky, V. (2013). Class-Specific Hough Forests for Object Detection. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_11
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4929-3_11
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4928-6
Online ISBN: 978-1-4471-4929-3
eBook Packages: Computer ScienceComputer Science (R0)