# Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection

arXiv: https://arxiv.org/abs/1706.03038 · HTML: https://mlsanity.com/paper/1706.03038/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 30 of 46)

- [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [Dogfight: Detecting Drones from Drones Videos](https://mlsanity.com/paper/2103.17242/) (2021)
- [Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?](https://mlsanity.com/paper/2312.04548/) (2023)
- [Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances](https://mlsanity.com/paper/2303.10280/) (2023)
- [RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning](https://mlsanity.com/paper/2409.13366/) (2024)
- [A Multi-viewpoint Outdoor Dataset for Human Action Recognition](https://mlsanity.com/paper/2110.04119/) (2021)
- [FAR: Fourier Aerial Video Recognition](https://mlsanity.com/paper/2203.10694/) (2022)
- [Progressive Transformation Learning for Leveraging Virtual Images in Training](https://mlsanity.com/paper/2211.01778/) (2022)
- [UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception](https://mlsanity.com/paper/2310.16255/) (2023)
- [Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata](https://mlsanity.com/paper/2209.00128/) (2022)
- [AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning](https://mlsanity.com/paper/2303.01589/) (2023)
- [PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition](https://mlsanity.com/paper/2304.06866/) (2023)
- [Fewer is More: Efficient Object Detection in Large Aerial Images](https://mlsanity.com/paper/2212.13136/) (2022)
- [Diversifying Human Pose in Synthetic Data for Aerial-view Human Detection](https://mlsanity.com/paper/2405.15939/) (2024)
- [MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition](https://mlsanity.com/paper/2303.02575/) (2023)
- [Real-time Aerial Detection and Reasoning on Embedded-UAVs](https://mlsanity.com/paper/2305.12414/) (2023)
- [Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery](https://mlsanity.com/paper/2404.01571/) (2024)
- [TK-Planes: Tiered K-Planes with High Dimensional Feature Vectors for Dynamic UAV-based Scenes](https://mlsanity.com/paper/2405.02762/) (2024)
- [Exploring the Impact of Synthetic Data for Aerial-view Human Detection](https://mlsanity.com/paper/2405.15203/) (2024)
- [Exploring the Potential of Synthetic Data to Replace Real Data](https://mlsanity.com/paper/2408.14559/) (2024)
- [Validation of object detection in UAV-based images using synthetic data](https://mlsanity.com/paper/2201.06629/) (2022)
- [ELSA: Evaluating Localization of Social Activities in Urban Streets using Open-Vocabulary Detection](https://mlsanity.com/paper/2406.01551/) (2024)
- [UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting](https://mlsanity.com/paper/2506.05011/) (2025)
- [DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition](https://mlsanity.com/paper/2212.03384/) (2022)
- [HawkI: Homography & Mutual Information Guidance for 3D-free Single Image to Aerial View](https://mlsanity.com/paper/2311.15478/) (2023)
- [SynPlay: Large-Scale Synthetic Human Data with Real-World Diversity for Aerial-View Perception](https://mlsanity.com/paper/2408.11814/) (2024)

## Abstract (arXiv)

Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios. We present Okutama-Action, a new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. As a result, our dataset is more challenging than existing ones, and will help push the field forward to enable real-world applications.