iccv iccv2013 iccv2013-190 iccv2013-190-reference knowledge-graph by maker-knowledge-mining
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Author: Markus Mathias, Rodrigo Benenson, Radu Timofte, Luc Van_Gool
Abstract: Detecting partially occluded pedestrians is challenging. A common practice to maximize detection quality is to train a set of occlusion-specific classifiers, each for a certain amount and type of occlusion. Since training classifiers is expensive, only a handful are typically trained. We show that by using many occlusion-specific classifiers, we outperform previous approaches on three pedestrian datasets; INRIA, ETH, and Caltech USA. We present a new approach to train such classifiers. By reusing computations among different training stages, 16 occlusion-specific classifiers can be trained at only one tenth the cost of one full training. We show that also test time cost grows sub-linearly.
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[6]. The nodes are constructed using a pool of 30 000 candidate regions. The full classifier consists of 2 000 weak clas- ×× sifiers. We train in 3 stages; the first stage randomly samples 5 000 negative samples, the second and third stage use bootstrapping to add 5 000 additional hard negatives each. To be faster and memory efficient we shrink the feature channels by a factor 4 (see [6, addendum]). The model window is of size 64 128 pixels, after shrinking it has size 16 32 pixels. Training t1i2m8ep i sx emles,a asfutererds on a desktop mssaiczhei1n6e× ×w3i2thp an eInls-. tel Core i7 870 CPU and a Nvidia GeForce GTX 590 GPU. 1155 1122