On the Sustained Tracking of Human Motion

Abstract

Tracking humans requires consideration of a number of challenges which include articulated motion estimation, self occlusion, and varying appearance. In this paper, we propose an algorithm for sustained tracking of humans, where we combine frame-to-frame articulated motion estimation with a per-frame body detection algorithm. The proposed approach can automatically recover from tracking error and drift. The frame-to-frame motion estimation algorithm replaces traditional dynamic models within a filtering framework. Stable and accurate per-frame motion is estimated via a image-gradient based algorithm that solves a linear constrained least squares system. The per-frame detector learns appearance of different body parts and `sketches' expected gradient maps to detect discriminant pose configurations in images. The resulting online algorithm is computationally efficient and has been widely tested on a large dataset of sequences of drivers in vehicles. It shows stability and sustained accuracy over thousands of frames.