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

arXiv: https://arxiv.org/abs/1904.02319 · HTML: https://mlsanity.com/paper/1904.02319/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 18 of 18)

- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [Unsupervised Domain Adaptation for Nighttime Aerial Tracking](https://mlsanity.com/paper/2203.10541/) (2022)
- [SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking](https://mlsanity.com/paper/2106.08816/) (2021)
- [SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking](https://mlsanity.com/paper/2303.04378/) (2023)
- [DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs](https://mlsanity.com/paper/2309.10491/) (2023)
- [Predictive Visual Tracking: A New Benchmark and Baseline Approach](https://mlsanity.com/paper/2103.04508/) (2021)
- [Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking](https://mlsanity.com/paper/2203.01516/) (2022)
- [Cine-AI: Generating Video Game Cutscenes in the Style of Human Directors](https://mlsanity.com/paper/2208.05701/) (2022)
- [PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework](https://mlsanity.com/paper/2211.11629/) (2022)
- [3D Human Reconstruction in the Wild with Collaborative Aerial Cameras](https://mlsanity.com/paper/2108.03936/) (2021)
- [BACTrack: Building Appearance Collection for Aerial Tracking](https://mlsanity.com/paper/2312.06136/) (2023)
- [Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker](https://mlsanity.com/paper/2401.04377/) (2024)
- [Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments](https://mlsanity.com/paper/2310.10863/) (2023)
- [Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones](https://mlsanity.com/paper/2407.02405/) (2024)
- [Enhancing Nighttime UAV Tracking with Light Distribution Suppression](https://mlsanity.com/paper/2409.16631/) (2024)
- [Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots](https://mlsanity.com/paper/2107.04924/) (2021)
- [Exploring the efficacy of neural networks for trajectory compression and the inverse problem](https://mlsanity.com/paper/2107.08849/) (2021)
- [Sparse Regularized Correlation Filter for UAV Object Tracking with adaptive Contextual Learning and Keyfilter Selection](https://mlsanity.com/paper/2205.03627/) (2022)

## Abstract (arXiv)

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