Abstract
In this paper, we propose an ensemble classification approach to the Pedestrian Detection (PD) problem, resorting to distinct input channels and Convolutional Neural Networks (CNN). This methodology comprises two stages: (i) the proposals extraction, and (ii) the ensemble classification. In order to obtain the proposals, we apply several detectors specifically developed for the PD task. Afterwards, these proposals are converted into different input channels (e.g. gradient magnitude, LUV or RGB), and classified by each CNN. Finally, several ensemble methods are used to combine the output probabilities of each CNN model. By correctly selecting the best combination strategy, we achieve improvements, comparatively to the single CNN models predictions.
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