We propose a simple yet effective approach to the problem of pedestrian detection which outperforms the current state-of-the-art. Our new features are built on the basis of low-level visual features and spatial pooling. Incorporating spatial pooling improves the translations] in variance and thus the robustness of the detection process. We then directly optimise the partial area under the ROC curve (pAUC) measure, which concentrates detection performance in the range of most practical importance. The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets. We advance state-of-the-art results by lowering the average miss rate from 13% to 11% on the INRIA benchmark, 41% to 37% on the ETH benchmark, 51% to 42% on the TUD-Brussels benchmark and 36% to 29% on the Caltech-USA benchmark.
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