Multi hypothesis track extraction and maintenance of GMTI sensor data

Koller, Jost; Ulmke, Martin

doi:10.1109/icif.2005.1592035

Cited by 8 publications

(2 citation statements)

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“…MULTIPLE HYPOTHESIS TRACKING FILTER WITH GHT RECOGNIZER MHT algorithm has been studied extensively in the literature because of its effectiveness and wide range of application [1][2][3][4]. Especially, Blackman [3] provides very good explanation on the MHT -its concept, implementation aspect, relations with other filters.…”

Section: Target Template and Contribution Setsmentioning

confidence: 99%

“…They find the time-correlation between measurements to form a track or a target trajectory. The MHT (multiple hypothesis tracking) or PDA (probabilistic data association) as representative data association filters [1][2][3][4], have been shown the ability to deal effectively with the measurement origin uncertainty as well as they stand on the rigorous mathematical foundation. However, since these filters require a priori knowledge on the pdf (probability density function) of targets and clutters, it is very hard to apply them to image tracking filter without probabilistic models of recognition algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Hypothesis Tracking in Generalized Hough Transformed Images

Whang

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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In this paper, a new image tracking filter incorporated with GHT (generalized Hough transform) object recognition algorithm is proposed. The GHT process to identify a target shape in the input image is modeled as a Gaussian pdf (probability density function) through in-depth study on the process. The pdf model for GHT is used to systematically determine the target detection threshold for the GHT images in consideration of the target detection probability as well as false alarm probability. The proposed probabilistic GHT process model makes it possible to directly apply the MHT (multiple hypothesis tracking) algorithm to the image tracking problem. The main advantage of the proposed NHT scheme lies with the efficient handling of apriori matching likelihood evaluated from the probabilistic GHT process model. Therefore, it can maintain the target tracking even in the presence of occlusion or clutter around the target-moving path. An illustrative image tracking example is provided to demonstrate the performance of the proposed MHT filter proposed based on the GHT probability model. The example shows that our MHT filter can effectively track a target in clutter environment.

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Section: Target Template and Contribution Setsmentioning

confidence: 99%