Vehicle based threat detection and tracking with lwir video

Abstract

A computer program product interacts with machine-readable mediums with instructions for automated target recognition. It captures a video of a specified region using detectors on a vehicle. Raw image frames from the video undergo pre-processing, followed by feeding through long and short-range target detection pipelines. Image frames are downscaled in the short-range pipeline, generating image windows and applying a trained convolutional neural network (CNN). Full resolution and non-redundant image chips are created. Detection region of interest (ROI) proposal lists are generated and analyzed with the CNN, producing frame detection lists with detected targets. These lists are stacked and processed using a multi-target Kalman filter, ultimately creating a track list of targets for monitoring.