Effective and Efficient Tracking and Ego-Motion Recovery for Mobile Cameras

Abstract

AbstractEstimating 3-D structure and camera motion from 2-D image sequences is an important problem in computer vision. In this paper we present an effective approach to tracking and recovery of ego-motion from an image sequence acquired by a single camera attached to a pedestrian. Our approach consists of two stages. In the first phase, human gait analysis is performed and human gait parameters are estimated by frame-by-frame analysis utilising a generalised least squares technique. In the second phase, the gait model is employed within a “predict-correct” framework using a maximum a posteriori expectation maximisation strategy to recover ego-motion and scene structure, while continuously refining the gait model. Experiments on synthetic and real image sequences confirm that the use of the gait model allows for effective tracking while also reducing the computational complexity.KeywordsRoll AngleGait ParameterGeneralise Little SquareFeature TrackingStructure From MotionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.