  Pedestrian detection is a popular research topic due to its paramount
importance for a number of applications, especially in the fields of
automotive, surveillance and robotics. Despite the significant improvements,
pedestrian detection is still an open challenge that calls for more and more
accurate algorithms. In the last few years, deep learning and in particular
convolutional neural networks emerged as the state of the art in terms of
accuracy for a number of computer vision tasks such as image classification,
object detection and segmentation, often outperforming the previous gold
standards by a large margin. In this paper, we propose a pedestrian detection
system based on deep learning, adapting a general-purpose convolutional network
to the task at hand. By thoroughly analyzing and optimizing each step of the
detection pipeline we propose an architecture that outperforms traditional
methods, achieving a task accuracy close to that of state-of-the-art
approaches, while requiring a low computational time. Finally, we tested the
system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a
forerunner computational brain of future self-driving cars.
