A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation
This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are a novel and real-time Deep Learning person detection and a standardization of personal space, that can be used with any path planer. In the first stage of the approach, we propose to cascade the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast and accurate Pedestrian Detection (PD). For the personal space definition (that can be defined as constraints associated with the robot's motion), we used a mixture of asymmetric Gaussian functions, to define the cost functions associated to each constraint. Both methods were evaluated individually. The final solution (including both the proposed pedestrian detection and the personal space constraints) was tested in a typical domestic indoor scenario, in four distinct experiments. The results show that the robot is able to cope with human-aware constraints, defined after common proxemics rules.