Part-Based Models for Finding People and Estimating Their Pose

Abstract

This chapter will survey approaches to person detection and pose estimation with the use of part-based models. After a brief introduction/motivation for the need for parts, the bulk of the chapter will be split into three core sections on Representation, Inference, and Learning. We begin by describing various gradient-based and color descriptors for parts. We next focus on representations for encoding structural relations between parts, describing extensions of classic pictorial structures models to capture occlusion and appearance relations. We will use the formalism of probabilistic models to unify such representations and introduce the issues of inference and learning. We describe various efficient algorithms designed for tree-structured models, as well as focusing on discriminative formalisms for learning model parameters. We finally end with applications of pedestrian detection, human pose estimation, and people tracking.