Advanced automated target recognition (ATR) and multi-target tracker (MTT) with electro-optical (EO) sensors

Hai-Wen Chen; Mark Reyes; Brent Marquand; David Robie

doi:10.1117/12.2567178

20 August 2020 Advanced automated target recognition (ATR) and multi-target tracker (MTT) with electro-optical (EO) sensors

Hai-Wen Chen, Mark Reyes, Brent Marquand, David Robie

Author Affiliations +

Proceedings Volume 11511, Applications of Machine Learning 2020; 115110V (2020) https://doi.org/10.1117/12.2567178
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

We are interested in ATR on various military target types such as military tanks and mobile rocket/missile launching vehicles from as high of UAV flying altitudes as possible. However, many of our current high-flying UAV datasets do not contain these types of military targets. Therefore, a high-fidelity target insertion (HFTI) tool has been developed for testing Advanced ATR on military targets. In this paper, we present results on development of Advanced ATR using the state-of-the-art Transfer Learning and Deep Learning (DL) Convolutional Neural Networks (CNN) target detection and recognition models for the military target detection and recognition. Large labelled training datasets have been generated by the newly developed HFTI tool to train the CNN ATR. We have developed and tested two different CNN ATRs: (1) detection-based YOLOv2 model and (2) segmentation-based U-Net model. Both ATRs have achieved promising performance. A Multi-Target Tracker (MTT) has also been developed to track military vehicles that were detected and recognized by the CNN U-Net ATR. In the presentation, we will show live videos for the ATR performance with accurate multiple (moving and static) targets recognition and tracking.

Conference Presentation

Citation Download Citation

Hai-Wen Chen, Mark Reyes, Brent Marquand, and David Robie "Advanced automated target recognition (ATR) and multi-target tracker (MTT) with electro-optical (EO) sensors", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110V (20 August 2020); https://doi.org/10.1117/12.2567178

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