Design of a morphological moving object signature and application to human identification

Many computer vision systems try to infer semantic information about a video scene content by looking at the time series of the silhouettes of the moving objects. This paper proposes a new inter-frame feature set (signature) based on piecewise surfacic descriptions of binary silhouettes. It captures the dynamics of moving objects and compacts it into a robust set of features suitable for classification. To assess its ability to represent motion information, we use it to build a complete gait recognition algorithm that we test on a database of 21 different subjects. To highlight the efficiency of our signature, we use frontal views instead of side views of persons, which is less discussed in literature and is considered to be harder as the movement of legs is not visible. In that context, the high recognition rates obtained (over 95% of correct identifications) proves that our signature is appropriate to describe moving objects.