Multi-Task Deep Learning for Depth-based Person Perception in Mobile Robotics

Abstract

Efficient and robust person perception is one of the most basic skills a mobile robot must have to ensure intuitive human-machine interaction. In addition to person detection, this also includes estimating various attributes, like posture or body orientation, in order to achieve user-adaptive behavior. However, given limited computing and battery capabilities on a mobile robot, it is inefficient to solve all perception tasks separately, especially when using computationally expensive deep neural networks. Therefore, we propose a multi-task system for person perception, comprising of a fast, depth- based region proposal and an efficient, lightweight deep neural network. Using a single network forward pass, the system simultaneously detects persons, classifies their body postures, and estimates the upper body orientations while retaining almost the same computation time as a single-task network. We describe how to handle a real-world multi-task scenario and conduct an extensive series of experiments in order to compare various network architectures and task weightings. We further show that multi-task learning improves the networks' performance compared to their single-task baselines. For training and evaluation, we combine an existing dataset for orientation estimation and a new, self-recorded dataset, consisting of more than 235,000 depth patches that is made publicly available to the research community.