From rigid templates to grammars: object detection with structured models

We develop models for localizing instances of a generic object category, such as cars or people, in images. We define these models using a grammar formalism. In this formalism compositional rules are used to encode models that can range in complexity from simple rigid templates to rich deformable part models with variable structure. A central contribution of this dissertation is an exploration along this axis, wherein we gradually enrich our object category representations. We demonstrate that these richer models lead to improved object detection performance on challenging datasets such as the PASCAL VOC Challenges. While building richer models, we would like to make use of existing training data and annotations. These annotations typically specify labels, such as object bounding boxes, that are "weak'' compared to the derivation trees produced by detection with a grammar model. We propose a new discriminative training framework that directly supports learning models from weakly-labeled examples. We show how to apply this framework to the problem of learning the parameters of a grammar model. This approach results in a top-performing method for detecting people in images. In order to achieve widespread use in research and applications, an object detection system must not only be accurate, but also fast. Along the line of efficient computation, we develop a technique for "compiling" one of our object models into a much faster detector that implements a cascade architecture. We show how to select the cascade thresholds in a way that is both safe and effective. We demonstrate that the cascaded detector produces detections 15x faster than the non-cascade approach with no loss in precision or recall.