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Robust pedestrian detection under deformation using simple boosted features
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DOI：10.1016/j.imavis.2017.02.007.png)
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Many existing methods for pedestrian detection have the limited detection performance in case of deformation such as large appearance variations. To overcome this limitation, we propose a novel pedestrian detection method that uses two low-level boosted features to detect pedestrians despite the presence of deformations. One is a boosted max feature (BMF) that uses a max operation to aggregate a selected pair of features to make them invariant to deformation. Another is a boosted difference feature (BDF) that uses a difference operation between a selected pair of features to improve localization accuracy of pedestrian detection. We incorporate a spatial pyramid pool method that uses multiple sized blocks to increase the richness of boosted features in a local region and use a RealBoost method to train a tree-structured classifier for the proposed pedestrian detection method. We also apply a region-of-interest method to the detected results to remove false positives effectively. Our proposed detector achieved log-average miss rates of 19.95%, 10.39%, 36.12%, and 39.57% on the Caltech-USA, INRIA, ETH, and TUD-Brussels dataset, respectively, which are the lowest among those of all state-of-the-art pedestrian detectors. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Regionlet
Pedestrian detection
Selective max pooling
Selective difference pooling
Boosted tree-structured classifier
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