Detection And Classification Of Target Formations In Infrared Image Sequences

Abstract

This paper describes a knowledge-based approach to the detection and classification of ground-based military target, formations in infrared (IR) image sequences at long range from an airborne platform. The motivation for this ap-proach is that at long range military vehicle targets in IR imagery typically consist of only a few pixels, have low contrast and exist in a high clutter environment making reliable detection, classification or identification of indivi-dual targets extremely difficult. Thus, the system detailed here detects targets based on their membership in a military target formation or convoy, relying on the structure or spatial organization of the formation and not individual target signatures. The system consists of two major components: Point Target Detection and Tracking (PTD); and Knowledge-Based Constraint Hierarchical Target Formation Detection and Classification (TFD). The first component detects and tracks both stationary and moving point targets which are passed on as candidates for clustering into target formations by a rule-based matching process in the second component. The PTD module makes use of image pre-processing, stationary and moving target detectors, image registration for sensor motion compensation, temporal processing for detection consistency over a large number of frames, in addition to inter-frame target and formation tracking. The TFD module consists of an expandable knowledge base containing characteristics of typical target formation types and a flexible set of rules that are used to match reference formations to actual formations in an image. The paper contains a complete description of the system methodology and provides experimental performance results of the system operating on real IR data.