@article{ref1,
title="Learning Complexity-Aware Cascades for Pedestrian Detection",
journal="IEEE transactions on pattern analysis and machine intelligence",
year="2019",
author="Cai, Zhaowei and Saberian, Mohammad Javad and Vasconcelos, Nuno",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="The problem of pedestrian detection is considered. The design of complexity-aware cascaded pedestrian detectors, combining features of very different complexities, is investigated. A new cascade design procedure is introduced, by formulating cascade learning as the Lagrangian optimization of a risk that accounts for both accuracy and complexity. A boosting algorithm, denoted as complexity aware cascade training (CompACT), is then derived to solve this optimization. CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified. This enables the use of features of vastly different complexities in a single detector. In result, the feature pool can be expanded to features previously impractical for cascade design, such as the responses of a deep convolutional neural network (CNN). This is demonstrated through the design of pedestrian detectors with a pool of features whose complexities span orders of magnitude. The resulting cascade generalizes the combination of a CNN with an object proposal mechanism: rather than a pre-processing stage, CompACT cascades seamlessly integrate CNNs in their stages. This enables accurate detection on the Caltech and KITTI datasets, at fairly fast speeds.<p /> <p>Language: en</p>",
language="en",
issn="0162-8828",
doi="10.1109/TPAMI.2019.2910514",
url="http://dx.doi.org/10.1109/TPAMI.2019.2910514"
}