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Model-Free Gait-Based Human Recognition in Video

  • Bir Bhanu
  • Ju Han
Part of the Advances in Pattern Recognition book series (ACVPR)

Abstract

In this chapter, the GEI-based general framework presented earlier is used for individual recognition in diverse scenarios.

Insufficient training data associated with an individual is a major problem in gait recognition due to the difficulty of the data acquisition. To address this issue, we not only compute real templates from training silhouette sequences directly, but also generate synthetic templates from training sequences by simulating silhouette distortion. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance.

Keywords

Feature Vector Training Sequence Bayesian Classifier Individual Recognition Human Activity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.Bourns College of EngineeringUniversity of CaliforniaRiversideUSA
  2. 2.Lawrence Berkeley National LaboratoryUniversity of CaliforniaBerkeleyUSA

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