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.
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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
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