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Genetic CONDENSATION for motion tracking

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Abstract

Tracking serves as a means to prepare data for pose estimation and action recognition. The CONDENSATION algorithm is a conditional density propagation method for motion tracking. This algorithm combines factored sampling with learned dynamic models to propagate an entire probability distributes for object position and shape over time. It can accomplish highly robust tracking of object motion. However, it usually requires a large number of samples to ensure a fair maximum likelihood estimation of the current state. In this paper, we use the mutation and crossover operators of the genetic algorithm to find appropriate samples. With this approach, we are able to improve robustness, accuracy and flexibility in CONDENSATION for visual tracking.

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Correspondence to Zhu Ye.

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Ye, Z., Liu, ZQ. Genetic CONDENSATION for motion tracking. Soft Comput 11, 349–354 (2007). https://doi.org/10.1007/s00500-006-0088-0

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  • DOI: https://doi.org/10.1007/s00500-006-0088-0

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