Learning and Matching Line Aspects for Articulated Objects

Abstract

Traditional aspect graphs are topology-based and are impractical for articulated objects. In this work we learn a small number of aspects, or prototypical views, from video data. Groundtruth segmentations in video sequences are utilized for both training and testing aspect models that operate on static images. We represent aspects of an articulated object as collections of line segments. In learning aspects, where object centers are known, a linear matching based on line location and orientation is used to measure similarity between views. We use K-medoid to find cluster centers. When using line aspects in recognition, matching is based on pairwise cues of relative location, relative orientation as well adjacency and parallelism. Matching with pairwise cues leads to a quadratic optimization that we solve with a spectral approximation. We show that our line aspect matching is capable of locating people in a variety of poses. Line aspect matching performs significantly better than an alternative approach using Hausdorff distance, showing merits of the line representation.