Long-duration fused feature learning-aided tracking

Richard Ivey; Joel Horn; Raimund Merkert

doi:10.1117/12.854007

28 April 2010 Long-duration fused feature learning-aided tracking

Richard Ivey, Joel Horn, Raimund Merkert

Proceedings Volume 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010; 77100M (2010) https://doi.org/10.1117/12.854007
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Multiple-hypothesis tracking (MHT) algorithms solve the report-to-track association problem^1-5 by accumulating kinematic evidence provided by one or more sensors over time to find likely correlations in the data. MHT technologies have long been applied to such problems as machine vision; automatic target tracking using radar moving target indicator (MTI) for ground, air, sea, and space vehicles; and video-based object tracking. However, relying on kinematic information alone to maintain reliable track in a multi-target scenario is problematic due to a plethora of issues such as sensor limitations, obscuration of the targets, or target/confuser proximity.^6-7 We present our track fusion algorithm, the Long-term Hypothesis Tree (LTHT) that solves the tracklet-to-tracklet association problem by using signature information to repair errors in kinematic tracks. LTHT provides a framework for using arbitrary target signatures such as spectral or shape characteristics to correct errors made by an MHT tracker. The LTHT represents high-level interactions among complex tracks by condensing kinematic track trajectories into a compact representation that can be efficiently maintained over much longer temporal scales than typical MHT trees.

Citation Download Citation

Richard Ivey, Joel Horn, and Raimund Merkert "Long-duration fused feature learning-aided tracking", Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, 77100M (28 April 2010); https://doi.org/10.1117/12.854007

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