A High-Performance Pedestrian Detector and Its Implementation on Embedded Systems for Hypermarket Environment

Abstract

Enabling autonomous driving in hypermarket environments is a new challenge. The whole scenario is very different from traditional outdoor autonomous driving. To navigate in hypermarket environments, the vehicles need to know where all the surrounded pedestrians are. In addition, the detection model must be small enough to be executed in real-time speed on embedded systems. Therefore, we present a high-performance convolutional neural network for detecting moving indoor pedestrians as well as its implementation on embedded systems in this paper. The proposed CNN model can achieve the same high accuracy as YOLO v3 at the cost of only 27% of the original model size. When implemented on an embedded system, i.e., Jetson Xavier, this work achieves 30 fps @ 360p video format.