# Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments

## TL;DR

This paper presents an autonomous aerial cinematography system that localizes and tracks moving targets in unstructured environments using vision-based methods, real-time mapping, and optimized camera planning, outperforming existing approaches.

## Contribution

It introduces a complete system combining vision-based target localization, real-time 3D mapping, and an advanced camera planner for autonomous drone cinematography in unstructured settings.

## Key findings

- System achieves state-of-the-art performance in robustness and real-time tracking.
- Successfully operates in unknown, unstructured environments without prior maps.
- Demonstrates effective target localization and smooth camera control in dynamic scenarios.

## Abstract

The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely controlling a drone while filming a moving target usually requires multiple expert human operators; hence the need for an autonomous cinematographer. Current approaches have severe real-life limitations such as requiring fully scripted scenes, high-precision motion-capture systems or GPS tags to localize targets, and prior maps of the environment to avoid obstacles and plan for occlusion. In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a vision-based algorithm for target localization; (2) a real-time incremental 3D signed-distance map algorithm for occlusion and safety computation; and (3) a real-time camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines. We evaluate robustness and real-time performance in series of field experiments and simulations by tracking dynamic targets moving through unknown, unstructured environments. Finally, we verify that despite removing previous limitations, our system achieves state-of-the-art performance. Videos of the system in action can be seen at https://youtu.be/ZE9MnCVmumc