Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

TL;DR

This paper presents a cooperative vision-based tracking system using multiple micro aerial vehicles with onboard deep neural networks, enabling real-time, accurate, and continuous person tracking in outdoor environments without remote processing.

Contribution

It introduces a novel cooperative multi-MAV system that fuses detections for improved real-time person tracking using onboard DNNs, addressing resolution and scale challenges.

Findings

Successfully tracked a person with two MAVs in real robot experiments.

Achieved continuous, high-accuracy tracking of distant humans.

Operates fully onboard without remote processing.

Abstract

Multi-camera full-body pose capture of humans and animals in outdoor environments is a highly challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. The key enabling-aspect of our approach is the on-board person detection and tracking method. Recent state-of-the-art methods based on deep neural networks (DNN) are highly promising in this context. However, real time DNNs are severely constrained in input data dimensions, in contrast to available camera resolutions. Therefore, DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, real-time, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs. First, each MAV fuses its own detections with those obtained by other MAVs to perform cooperative visual tracking. This allows for predicting future poses of the tracked person, which are used to selectively process only the relevant regions of future images, even at high resolutions. Consequently, using our DNN-based detector we are able to continuously track even distant humans with high accuracy and speed. We demonstrate the efficiency of our approach through real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. Our solution runs fully on-board our MAV's CPU and GPU, with no remote processing. ROS-based source code is provided for the benefit of the community.