Predictive visual tracking

Due to delays in image acquisition and processing, prediction is a critical factor for successful visual tracking of moving objects (both for humans and for vision machines). This paper explores some alternative techniques for predicting object motion for the purpose of tracking with an active camera system. In particular, one of our goals is to develop a system that will track an object undergoing “random” motion quite well, but that will track much better (at higher speeds with less lag) if the object settles into a periodic motion of some kind. Rather than identify parameters for specific signal models to accomplish this, we propose to use a finite set of previous samples of the target signal for the signal model. The advantages and problems associated with this approach are discussed. Results of experiments using different prediction algorithms with TRICLOPS, a high-performance active vision system, are also presented.