Casting scene context into a Bayesian framework for automated target recognition

Richard Shilling; Andrew Myers; Faries Tadros; Brian Dina

doi:10.1117/12.2664774

13 June 2023 Casting scene context into a Bayesian framework for automated target recognition

Richard Shilling, Andrew Myers, Faries Tadros, Brian Dina

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 1252106 (2023) https://doi.org/10.1117/12.2664774
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Automated target detection and recognition (ATDR) algorithms solely based on sensor data have seen great strides in improvement, especially with the on-set of deep learning neural networks, multi-sensor and multimodal fusion techniques. However, ATR applied just on imagery with few-pixels-on-targets in highly-cluttered environments remains a tough problem. Rather than focusing on imagery as the only input to an ATDR process, in this paper, we turn our attention to using contextual and heterogeneous information to help aid in improving ATDR accuracy. We treat scene context as a collection of random variables that can then be cast into a Bayesian framework. Specifically, targets likelihoods given the context are estimated by an ensemble training process. Then statistical inference is applied to update the probability vector of the target estimates. For low-observability cases on the targets, this can dramatically improve the accuracy of the true target type. In this paper, we identify some of these contexts and apply it from the output of an emulated ATDR image-only process and report results.

Conference Presentation

Citation Download Citation

Richard Shilling, Andrew Myers, Faries Tadros, and Brian Dina "Casting scene context into a Bayesian framework for automated target recognition", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 1252106 (13 June 2023); https://doi.org/10.1117/12.2664774

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available