  Convolutional neural networks (CNNs) have demonstrated their superiority in
numerous computer vision tasks, yet their computational cost results
prohibitive for many real-time applications such as pedestrian detection which
is usually performed on low-consumption hardware. In order to alleviate this
drawback, most strategies focus on using a two-stage cascade approach.
Essentially, in the first stage a fast method generates a significant but
reduced amount of high quality proposals that later, in the second stage, are
evaluated by the CNN. In this work, we propose a novel detection pipeline that
further benefits from the two-stage cascade strategy. More concretely, the
enriched and subsequently compressed features used in the first stage are
reused as the CNN input. As a consequence, a simpler network architecture,
adapted for such small input sizes, allows to achieve real-time performance and
obtain results close to the state-of-the-art while running significantly faster
without the use of GPU. In particular, considering that the proposed pipeline
runs in frame rate, the achieved performance is highly competitive. We
furthermore demonstrate that the proposed pipeline on itself can serve as an
effective proposal generator.
