Real-Time multiple point target detection and tracking in infrared imageryg±

Infrared (IR) point target detection and tracking is challenging due to lack of texture and detailed information of small dim targets. The problem becomes even more challenging when there is a real-Time requirement too. A key goal in robust detection is to reduce missed detections (MD) and false alarms (FA). Complex traditional state-of-The-Art point target detection algorithms may give accurate results but typically incur larger execution times which renders them unsuitable for real-Time applications. On the other hand, deep learning methods for point target detection do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-Time detection and tracking of multiple point targets in diverse background conditions. We apply a 'top-hat' operator as the first stage of our detection algorithm and this is followed by a systematic thresholding scheme to yield a good balance between MD and FA. This is followed by a methodology to find the exact target position from the detections, and a track association scheme in conjunction with Kalman filter for state estimation. Based on the proposed approach for target detection and track association, we perform 2D tracking of image coordinates as well as 3D tracking of azimuth and elevation angles. We verify the effectiveness of our method on 12 different IR image sequences over existing state-of-The-Art methods in terms of accuracy as well as speed.