© 2014 Elsevier Ltd. All rights reserved. We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. To minimize the number of part-to-image comparisons we propose a multiple-resolutions hierarchical part-based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. The method yields a ten-fold speedup over the standard dynamic programming approach and, combined with the cascade-of-parts approach, a hundred-fold speedup in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
- Object detection
- Object recognition