Abstract
Gait has become a popular trait for biometric person recognition/re-identification. This is due to its advantage of being captured without any subject cooperation. This made it suitable especially for video surveillance applications. However, the gait features obtained in such scenarios depends on the observed walking direction of the subject. In this paper, we deal with the problem related to walking direction estimation in unconstrained environments. Covariates factors (i.e. carrying different types of bag, clothing) affect considerably the accuracy of walking direction estimation problem. Therefore, we have proposed a solution which is suitable for both real time application and unconstrained environment where the user walking direction is different and affected by covariates factors. The discriminative power of this solution is verified through experiments. The performance of this method was evaluated on the CASIA-B database. Experimental results prove the effectiveness of our proposed walking direction estimation method.
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