# AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning

arXiv: https://arxiv.org/abs/2303.01589 · HTML: https://mlsanity.com/paper/2303.01589/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [FNet: Mixing Tokens with Fourier Transforms](https://mlsanity.com/paper/2105.03824/) (2021)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers](https://mlsanity.com/paper/2205.03436/) (2022)
- [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021)
- [More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation](https://mlsanity.com/paper/1912.00869/) (2019)
- [P-CNN: Pose-based CNN Features for Action Recognition](https://mlsanity.com/paper/1506.03607/) (2015)
- [Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection](https://mlsanity.com/paper/1706.03038/) (2017)
- [SmallBigNet: Integrating Core and Contextual Views for Video Classification](https://mlsanity.com/paper/2006.14582/) (2020)
- [Tiny Video Networks](https://mlsanity.com/paper/1910.06961/) (2019)
- [Efficient Video Classification Using Fewer Frames](https://mlsanity.com/paper/1902.10640/) (2019)
- [Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning](https://mlsanity.com/paper/2205.14833/) (2022)
- [Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition](https://mlsanity.com/paper/1807.03848/) (2018)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition](https://mlsanity.com/paper/1901.02602/) (2019)
- [Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction](https://mlsanity.com/paper/2003.04772/) (2020)
- [TinyVIRAT: Low-resolution Video Action Recognition](https://mlsanity.com/paper/2007.07355/) (2020)
- [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)
- [Action Recognition: From Static Datasets to Moving Robots](https://mlsanity.com/paper/1701.04925/) (2017)

## Built on by (most cited first, 8 of 8)

- [PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition](https://mlsanity.com/paper/2304.06866/) (2023)
- [MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition](https://mlsanity.com/paper/2303.02575/) (2023)
- [DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing Neural Radiance Fields](https://mlsanity.com/paper/2303.04322/) (2023)
- [Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience](https://mlsanity.com/paper/2505.05515/) (2025)
- [Heatmap Pooling Network for Action Recognition from RGB Videos](https://mlsanity.com/paper/2512.03837/) (2025)
- [SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition](https://mlsanity.com/paper/2305.12437/) (2023)
- [FALCON: Future-Aware Learning with Contextual Object-Centric Pretraining for UAV Action Recognition](https://mlsanity.com/paper/2409.18300/) (2024)
- [UAV-OVO: Out-of-Viewpoint Generalization in UAV Action Recognition](https://mlsanity.com/paper/2605.25615/) (2026)

## Abstract (arXiv)

We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately. This makes it easier to extract the key features and reduces the computational overhead. We also present an efficient temporal reasoning algorithm to capture the action information along the spatial and temporal domains within a controllable computational cost. Our approach has been implemented and evaluated both on the desktop with high-end GPUs and on the low power Robotics RB5 Platform for robots and drones. In practice, we achieve 6.1-7.4% improvement over SOTA in Top-1 accuracy on the RoCoG-v2 dataset, 8.3-10.4% improvement on the UAV-Human dataset and 3.2% improvement on the Drone Action dataset.