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Drone systems have been deployed by various law enforcement agencies tomonitor hostiles, spy on foreign drug cartels, conduct border controloperations, etc. This paper introduces a real-time drone surveillance system toidentify violent individuals in public areas. The system first uses the FeaturePyramid Network to detect humans from aerial images. The image region with thehuman is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) networkfor human pose estimation. The orientations between the limbs of the estimatedpose are next used to identify the violent individuals. The proposed deepnetwork can learn meaningful representations quickly using ScatterNet andstructural priors with relatively fewer labeled examples. The system detectsthe violent individuals in real-time by processing the drone images in thecloud. This research also introduces the aerial violent individual dataset usedfor training the deep network which hopefully may encourage researchersinterested in using deep learning for aerial surveillance. The pose estimationand violent individuals identification performance is compared with thestate-of-the-art techniques.

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