Online Approximate Model Representation Based on Scale-Normalized and Fronto-Parallel Appearance

Abstract

Various object representations have been widely used for many tasks such as object detection, recognition, and tracking. Most of them requires an intensive training process on large database which is collected in advance, and it is hard to add models of a previously unobserved object which is not in the database. In this paper, we investigate how to create a representation of a new and unknown object online, and how to apply it to practical applications like object detection and tracking. To make it viable, we utilize a sensor fusion approach using a camera and a single-line scan LIDAR. The proposed representation consists of an approximated geometry model and a viewpoint-scale invariant appearance model which makes to extremely simple to match the model and the observation. This property makes it possible to model a new object online, and provides a robustness to viewpoint variation and occlusion. The representation has benefits of both an implicit model (referred to as a view-based model) and an explicit model (referred to as a shape-based model). Intensive experiments using synthetic and real data demonstrate the viability of the proposed object representation in both modeling and detecting/tracking objects.