Bootstrap Initialization of Nonparametric Texture Models for Tracking

Abstract. In bootstrap initialization for tracking, we exploit a weak prior model used to track a target to learn a stronger model, without manual intervention. We define a general formulation of this problem and present a simple taxonomy of such tasks. The formulation is instantiated with algorithms for bootstrap ini- tialization in two domains: In one, the goal is tracking the position of a face at a desktop; we learn color models of faces, using weak knowledge about the shape and movement of faces in video. In the other task, we seek coarse estimates of head orientation; we learn a person-specific el- lipsoidal texture model for heads, given a generic model. For both tasks, we use nonparametric models of surface texture. Experimental results verify that bootstrap initialization is feasi- ble in both domains. We find that (1) independence assumptions in the learning process can be violated to a significant degree, if enough data is taken; (2) there are both domain-independent and domain-specific means to mitigate learning bias; and (3) repeated bootstrapping does not necessarily result in increasingly better models. 1 Introduction Often, we know something about the target of a tracking task in advance, but specific details about the target will be unknown. For example, in desktop in- terfaces, we are likely to be interested in the moving ellipsoid that appears in the image, but we may not know the user's skin color, 3D shape, or the particu- lar geometry of the facial features. If we could learn this additional information during tracking, we could use it to track the same objects more accurately, more eAEciently, or more robustly