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1 LEAR - Learning and recognition in vision Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble

Abstract : Detecting people in images is a key problem for video indexing, browsing and retrieval. The main difficulties are the large appearance variations caused by action, clothing, illumination, viewpoint and scale. Our goal is to find people in static video frames using learned models of both the appearance of body parts head, limbs, hands, and of the geometry of their assemblies. We build on Forsyth & Fleck-s general ‘body plan- methodology and Felzenszwalb & Huttenlocher-s dynamic programming approach for efficiently assembling candidate parts into ‘pictorial structures-. However we replace the rather simple part detectors used in these works with dedicated detectors learned for each body part using Support Vector Machines SVMs or Relevance Vector Machines RVMs. We are not aware of any previous work using SVMs to learn articulated body plans, however they have been used to detect both whole pedestrians and combinations of rigidly positioned subimages typically, upper body, arms, and legs in street scenes, under a wide range of illumination, pose and clothing variations. RVMs are SVM-like classifiers that offer a well-founded probabilistic interpretation and improved sparsity for reduced computation.We demonstrate their benefits experimentally in a series of results showing great promise for learning detectors in more general situations.

Keywords : kernel methods object recognition image and video indexing grouping and segmentation statistical pattern recognition kernel methods.





Author: Rémi Ronfard - Cordelia Schmid - William Triggs -

Source: https://hal.archives-ouvertes.fr/



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