Probabilistic 3D Tracking: Rollator Users' Leg Pose from Coronal Images

Understanding the human gait is an important objective towards improving elderly mobility. In turn, gait analyses largely depend on kinematic and dynamic measurements. While the majority of current markerless vision systems focus on estimating 2D and 3D walking motion in the sagittal plane, we wish to estimate the 3D pose of rollator users' lower limbs from observing image sequences in the coronal (frontal) plane. Our apparatus poses a unique set of challenges: a single monocular view of only the lower limbs and a frontal perspective of the rollator user. Since motion in the coronal plane is relatively subtle, we explore multiple cues within a Bayesian probabilistic framework to formulate a posterior estimate for a given subject's leg limbs. This paper describes four cues based on three features to formulate a pose estimate: image gradients, colour and anthropometric symmetry. Our appearance model is applied within a non-parametric (particle) filtering system to track the lower limbs. Our tracking system does not rely on any detection for automatic initialization. Preliminary experiments are promising, showing that the algorithm may provide an indication of relative depth for each lower limb.