Skip to Main Content
Door detection by using wearable cameras helps people with severe vision impairment to independently access unknown environments. The goal of this paper is to robustly detect different doors and classify them as office doors, elevators, exits, etc. These tasks are challenging due to the factors: 1) small inter-class variations of different objects such as office doors and elevators, 2) only part of an object is captured due to occlusions or continuous camera moving of a mobile system. To overcome the above challenges, we propose a Hierarchical Compositional Model (HCM) approach which incorporates context information into the model decomposition process of a part-based HCM to handle partially captured objects as well as large intra-class variations in different environments. Our preliminary experimental results demonstrate promising performance on doors detection over a wide range of scales, view points, and occlusions.