Tracking objects in video-based education using an enhanced particle filter

Abstract

Visual tracking is of great importance in multimedia technology enhanced learning. Many human-machine interaction based learning/teaching activities need tracking of specific object. Particle filter has grown to be a standard framework for visual tracking in the past decades. One of its key issues is the design of the proposal distribution which can greatly affect the performance of particle trackers. In this paper we propose an enhanced particle filter for robust visual tracking. First, we propose a new particle filter using two proposal distributions to generate particles, that is, the unscented Kalman filter and the transition prior. Second, we introduce the locality sensitive histogram (LSH) and color based appearance model to deal with the appearance variation within the particle filter tracking framework. Third, by combining our new particle filter and the LSH and color based appearance model, we develop a robust tracking algorithm. Experimental results show that our tracking algorithm is better than or not worse than several other tracking algorithms over several public sequences.