Long-duration fused feature learning-aided tracking
Richard Ivey, Joel Horn, Raimund Merkert · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Multiple-hypothesis tracking (MHT) algorithms solve the report-to-track association problem1-5 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.