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The Fastest Deformable Part Model for Object Detection
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal
doi:10.1109/cvpr.2014.320
dblp:conf/cvpr/YanLWL14
fatcat:czuimg6sgfhl3mpcq3b2qknquy