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

TL;DR

This paper presents a real-time drone surveillance system that detects violent individuals in public areas using a novel ScatterNet Hybrid Deep Learning network for pose estimation and orientation analysis, enhancing aerial security capabilities.

Contribution

It introduces a new deep learning approach combining ScatterNet and structural priors for efficient human pose estimation from aerial images, enabling violence detection with fewer labeled data.

Findings

System achieves real-time detection of violent individuals.

The proposed network outperforms state-of-the-art pose estimation methods.

Introduces a new aerial violent individual dataset for training and benchmarking.

Abstract

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.