Tracking Using Multiple Linear Searches and Motion Direction Sampling

Recent work in visual tracking has focussed on modelling target appearance, while using comparatively simple search methods to match those models to image data. Knowledge of the target's likely motion can both significantly reduce the search space and support more effective search strategies. We propose a new approach to target location which utilises sparse estimates of motion direction derived from local features to guide the generation of particles by a Markov Chain Monte Carlo (MCMC) based particle filter. The standard two-dimensional random walk is replaced by a series of one-dimensional searches in directions determined by the distribution of local feature motions. Two algorithms based on this approach are presented and evaluated. Experiments on both artificial and publically available, real image sequences show that the highest accuracy is obtained by sampling motion direction. The resulting algorithm successfully handles motion variations and reduces the likelihood that the tracker will be trapped in local extrem a when the target moves close to or is partially occluded by similar objects.