(a) (b) (c) (d) (e) (f) (g) Figure 6. Our HOG detectors cue mainly on silhouette contours (especially the head, shoulders and feet). The most active blocks are centred on the image background just outside the contour. (a) The average gradient image over the training examples. (b) Each “pixel” shows the maximum positive SVM weight in the block centred on the pixel. (c) Likewise for the negative SVM weights. (d) A test image. (e) It’s computed R-HOG descriptor. (f,g) The R-HOG descriptor weighted by respectively the positive and the negative SVM weights. would help to improve the detection results in more general situations. Acknowledgments. This work was supported by the Euro- pean Union research projects ACEMEDIA and PASCAL. We thanks Cordelia Schmid for many useful comments. SVM- Light [10] provided reliable training of large-scale SVM’s. References [1] S. Belongie, J. Malik, and J. Puzicha. Matching shapes. The 8th ICCV, Vancouver, Canada, pages 454–461, 2001. [2] V. de Poortere, J. Cant, B. Van den Bosch, J. de Prins, F. Fransens, and L. Van Gool. Efficient pedes- trian detection: a test case for svm based categorization. Workshop on Cognitive Vision, 2002. Available online: http://www.vision.ethz.ch/cogvis02/. [3] P. Felzenszwalb and D. Huttenlocher. Efficient matching of pictorial structures. CVPR, Hilton Head Island, South Car- olina, USA, pages 66–75, 2000. [4] W. T. Freeman and M. Roth. Orientation histograms for hand gesture recognition. Intl. Workshop on Automatic Face- and Gesture- Recognition, IEEE Computer Society, Zurich, [10] T. Joachims. Making large-scale svm learning practical. In B. Schlkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. The MIT Press, Cambridge, MA, USA, 1999. [11] Y. Ke and R. Sukthankar. Pca-sift: A more distinctive rep- resentation for local image descriptors. CVPR, Washington, DC, USA, pages 66–75, 2004. [12] D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91–110, 2004. [13] R. K. McConnell. Method of and apparatus for pattern recog- nition, January 1986. U.S. Patent No. 4,567,610. [14] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. PAMI, 2004. Accepted. [15] K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. IJCV, 60(1):63–86, 2004. [16] K. Mikolajczyk, C. Schmid, and A. Zisserman. Human detec- tion based on a probabilistic assembly of robust part detectors. The 8th ECCV, Prague, Czech Republic, volume I, pages 69– 81, 2004. [17] A. Mohan, C. Papageorgiou, and T. Poggio. Example-based 8 https://hal.inria.fr/file/index/docid/548512/filename/hog_cvpr2005.pdf