Improving the performance of pedestrian detectors using convolutional learning

We present new achievements on the use of deep convolutional neural networks (CNN) in the problem of pedestrian detection (PD). In this paper, we aim to address the following questions: (i) Given non-deep state-of-the-art pedestrian detectors (e.g. ACF, LDCF), is it possible to improve their top performances?; (ii) is it possible to apply a pre-trained deep model to these detectors to boost their performances in the PD context? In this paper, we address the aforementioned questions by cascading CNN models (pre-trained on Imagenet) with state-of-the-art non-deep pedestrian detectors. Furthermore, we also show that this strategy is extensible to different segmentation maps (e.g. RGB, gradient, LUV) computed from the same pedestrian bounding box (i.e. the proposal). We demonstrate that the proposed approach is able to boost the detection performance of state-of-the-art non-deep pedestrian detectors. We apply the proposed methodology to address the pedestrian detection problem on the publicly available datasets INRIA and Caltech.