An Optimization Framework for Real-Time Appearance-Based Tracking under Weak Perspective

In this work, we present a framework for tracking objects in changing views by finding the subwindow most likely to be the object using Haar-like features selected by AdaBoost as the representation. Probabilistic AdaBoost [14] is used to derive the objective function. In addition, the projective warping of 2D features is used to track 3D objects in non-frontal views in real time. Transformed 2D features can approximate relatively flat object structures such as the two eyes in a face. In this paper, it is shown that, under weak perspective projection, the projective warping of a rectangle feature can be approximated by a similarity transform with an additional free parameter. Since features in non-frontal views are computed on-the-fly by projective transforms under weak perspective projection, our framework requires only frontal-view training samples to track objects in multiple views.