Bayesian multi-target tracking and sequential object recognition

Walter Armbruster

doi:10.1117/12.776660

14 April 2008 Bayesian multi-target tracking and sequential object recognition

Walter Armbruster

Proceedings Volume 6967, Automatic Target Recognition XVIII; 69670S (2008) https://doi.org/10.1117/12.776660
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

The paper considers the following problem: given a 3D model of a reference target and a sequence of images of a 3D scene, identify the object in the scene most likely to be the reference target and determine its current pose. Finding the best match in each frame independently of previous decisions is not optimal, since past information is ignored. Our solution concept uses a novel Bayesian framework for multi target tracking and object recognition to define and sequentially update the probability that the reference target is any one of the tracked objects. The approach is applied to problems of automatic lock-on and missile guidance using a laser radar seeker. Field trials have resulted in high target hit probabilities despite low resolution imagery and temporarily highly occluded targets.

Citation Download Citation

Walter Armbruster "Bayesian multi-target tracking and sequential object recognition", Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670S (14 April 2008); https://doi.org/10.1117/12.776660

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