# Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

arXiv: https://arxiv.org/abs/1806.00746 · HTML: https://mlsanity.com/paper/1806.00746/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 20 of 20)

- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles](https://mlsanity.com/paper/2104.00946/) (2021)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification](https://mlsanity.com/paper/2401.02634/) (2024)
- [Audio-visual Representation Learning for Anomaly Events Detection in Crowds](https://mlsanity.com/paper/2110.14862/) (2021)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance](https://mlsanity.com/paper/2104.06781/) (2021)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization](https://mlsanity.com/paper/2306.07576/) (2023)
- [Active Transfer Learning for Efficient Video-Specific Human Pose Estimation](https://mlsanity.com/paper/2311.05041/) (2023)
- [Person Recognition in Aerial Surveillance: A Decade Survey](https://mlsanity.com/paper/2511.17674/) (2025)
- [Student Dangerous Behavior Detection in School](https://mlsanity.com/paper/2202.09550/) (2022)
- [ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI](https://mlsanity.com/paper/2201.10366/) (2022)
- [Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics](https://mlsanity.com/paper/2302.06048/) (2023)
- [Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile](https://mlsanity.com/paper/2311.12829/) (2023)
- [AVAR-Net: A Lightweight Audio-Visual Anomaly Recognition Framework with a Benchmark Dataset](https://mlsanity.com/paper/2510.13630/) (2025)
- [UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV](https://mlsanity.com/paper/2208.06702/) (2022)
- [Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks](https://mlsanity.com/paper/2411.01348/) (2024)
- [DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person Recognition](https://mlsanity.com/paper/2505.04793/) (2025)

## Abstract (arXiv)

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.