Invariant Classification of Gait Types

This paper presents a method of classifying human gait in an invariant manner based on silhouette comparison. A database of artificially generated silhouettes is created representing the three main types of gait, i.e. walking, jogging, and running. Silhouettes generated from different camera angles are included in the database to make the method invariant to camera viewpoint and to changing directions of movement. The extraction of silhouettes are done using the Codebook method and silhouettes are represented in a scale- and translation-invariant manner by using shape contexts and tangent orientations. Input silhouettes are matched to the database using the Hungarian method. A classifier is defined based on the dissimilarity between the input silhouettes and the gait actions of the database. The overall recognition rate is 88.2% on a large and diverse test set. The recognition rate is better than that achieved by other approaches applied to similar data.