Model generation for robust object tracking based on temporally stable regions

Tracking and recognition of objects in video sequences suffer from difficulties in learning appropriate object models. Often a high degree of supervision is required, including manual annotation of many training images. We aim at unsupervised learning of object models and present a novel way to build models based on motion information extracted from video sequences. We require a coarse delineation of moving objects and subsequent segmentation of these motion areas into regions as preprocessing steps and analyze the resulting regions with respect to their stable detection over many frames. These 'temporally stable regions' are then used to build graphs of reliably detected object parts which form our model. Our approach combines the feature- based analysis of feature vectors for each region with the structural analysis of the graphical object models. Our experiments demonstrate the capabilities of this novel method to build object models for people and to robustly track them, but the method is in general applicable to learn object models for any object category, provided that the object moves and is observed by a stationary camera.