Object and Pattern Detection in Video Sequences

This thesis presents a general trainable framework for object detection in static images of cluttered scenes and a novel motion based extension that enhances per-formance over video sequences. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as input to a support vector machine classifier. The paradigm we present successfully handles the major difficulties of object detection: overcoming the in-class variability of complex classes such as faces and pedestrians and providing a very low false detection rate, even in unconstrained en-vironments. We demonstrate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection; we extend the methodology to the domain of people which, unlike faces, vary greatly in color