Real-time Aerial Suspicious Analysis (ASANA) System and Method for Identification of Suspicious individuals in public areas
OwnerAmarjot Singhinventor
Life of this patent
Estimating the term…
Abstract
A real-time aerial suspicious analysis (ASANA) system and method for identifying individuals engaged in suspicious activities in public areas is provided in the present invention. The aerial suspicious analysis (ASANA) uses a Drone for constant capturing and recording images/videos, and/or can be activated to capture/record based on a specific schedule and/or event; a YOLO detector to detect the individuals; a SHDL network for individual pose estimation, and then classification is performed of the estimated pose to identify the suspicious/violent individuals.
Claims20
An aerial suspicious analysis (ASANA) system for identifying suspicious individuals in public areas or in a controlled environment, the system comprising:
- at least one drone configured for capturing/recording one or more aerial images;
- at least one computing system for performing analysis on the aerial images for extracting features from the captured/recorded image;
- a YOLO detector for detecting the individuals by performing analysis on the aerial images for extracting features from the captured/recorded image;
- a ScatterNet Hybrid Deep Learning (SHDL) Network for pose estimation of the detected individuals, where the ScatterNet Hybrid Deep Learning (SHDL) Network identifies fourteen key-points of a human body to form a skeleton structure of the detected individuals;
- and
- a three dimensional (3D) ResNet for classification of the estimated pose to determine whether the suspicious individuals exist in the estimated pose,
- wherein the ScatterNet Hybrid Deep Learning (SHDL) Network is trained with an Aerial Violent Individual (AVI) Dataset to perform analysis of the identified key-points and the 3D ResNet is trained on the estimated skeletons for at least five suspicious or violent activities (Punching, Stabbing, Shooting, Kicking, and Strangling) and one neutral activity, in the Aerial Violent Individual (AVI) Dataset, to perform multi-class classification
- wherein the Aerial Violent Individual (AVI) Dataset is composed of thousands of images and thousands of individuals engaged in one or more suspicious or violent activities.
A method for identifying suspicious individuals in public areas or in a controlled environment, the method comprising:
- capturing/recording one or more aerial images using one or more drones;
- performing analysis on the aerial images for extracting features from the captured/recorded image;
- detecting individuals using a YOLO detector;
- pose estimation of the individuals using a ScatterNet Hybrid Deep Learning (SHDL) Network;
- identifying fourteen key-points of a human body to form a skeleton structure of the detected individuals;
- and
- classifying of the estimated pose using a three dimensional (3D ResNet for determining whether the suspicious individuals exist in the estimated pose,
- wherein the ScatterNet Hybrid Deep Learning (SHDL) Network is trained with an Aerial Violent Individual (AVI) Dataset to perform analysis of the identified key-points, and the 3D ResNet is trained on the estimated skeletons for at least five suspicious or violent activities (Punching, Stabbing, Shooting, Kicking, and Strangling) and one neutral activity, in the Aerial Violent Individual (AVI) Dataset, to perform multi-class classification
- wherein the Aerial Violent Individual (AVI) Dataset is composed of thousands of images and thousands of individuals engaged in one or more suspicious or violent activities.
Dependencies are inferred from the claim text.