Quadruped gait analysis using sparse motion information

In this paper we propose a system that recognises gait and quadruped structure from a sparse set of tracked points. In this work the motion information is derived from dynamic wildlife film footage and is consequently extremely complex and noisy. The gait analysis is carried out on footage that contains quadrupeds, usually walking in profile to the camera, however, this part of the system is pose independent and useful for gait detection in general. The dominant motion is assumed to be generated by the background and its relationship to the camera motion, enabling its removal as an initial step. Along with frequency analysis, an eigengait model is used as a template to synchronise clusters of points and to established an underlying spatio-temporal structure. Given this synchronised structure, further tracking observations are used to deform the structure to better fit the overall motion. We demonstrate that the use of an eigengait model enables the spatio-temporal localisation of walking animals and assists in overcoming difficulties caused by occlusion, tracking failure and noisy measurements.