Gait Representation Using Flow Fields

Gait is characterised by the relative motions between different body parts during walking. However, most recently proposed gait representation approaches such as Gait Energy Image (GEI) and Motion Silhouettes Image (MSI) capture only the motion intensity information whilst ignoring the equally important but less reliable information about the direction of relative motion. They thus essentially sacrifice discriminative power in exchange for robustness. In this paper, we propose a novel gait representation based on optical flow fields computed from normalized and centred person images over a complete gait cycle. In our representation, both the motion intensity and the motion direction information is captured in a set of motion descriptors. To achieve robustness against noise, instead of relying on the exact value of the flow vectors, the flow direction is discretised and a histogram based direction representation is formulated. Compared to the existing model-free gait representations, our representation is not only more discriminative, but also less sensitive to changes in various covariate conditions including clothing, carrying, shoe, and speed. Extensive experiments on both indoor and outdoor public datasets have been carried out to demonstrate that our representation outperforms the state-of-the-art.