SURF-based human tracking algorithm for a human-following mobile robot

Tracking a human in a video sequence is an important function of a human-following robot. This paper aims at developing a robust vision-based algorithm using point-based features, like SURF, which can track a human under challenging conditions including variation in illumination, pose change, full or partial occlusion and abrupt camera motion. Since the point-based methods use tracking-by-detection framework, the major problem lies in finding sufficient number of descriptors in subsequent frames as the target undergoes the above mentioned variations. This paper looks into the problem of constructing an object model which can evolve over time to deal with short-term changes while maintaining stability on a longer term. The object model is updated by propagating useful descriptors from past templates onto the current template using affine transformation. An SVM classifier along with a Kalman Filter predictor is used to differentiate between a case of pose change and a case of occlusion (partial/full). An attempt is also made to detect pose change due to out-of-plane rotations which is a difficult problem and lead to frequent tracking failures. The efficacy of the algorithm is demonstrated through various simulation and experimental results.