Boosting with a Joint Feature Pool from Different Sensors

This paper introduces a new way to apply boosting to a joint feature pool from different sensors, namely 3D range data and color vision. The combination of sensors strengthens the systems universality, since an object category could be partially consistent in shape, texture or both. Merging of different sensor data is performed by computing a spatial correlation on 2D layers. An AdaBoost classifier is learned by boosting features competitively in parallel from every sensor layer. Additionally, the system uses new corner-like features instead of rotated Haar-like features, in order to improve real-time classification capabilities. Object type dependent color information is integrated by applying a distance metric to hue values. The system was implemented on a mobile robot and trained to recognize four different object categories: people, cars, bicycle and power sockets. Experiments were conducted to compare system performances between different merged and single sensor based classifiers...

• Type: Journal
• Year: 2009
• Where: ICVS
• Authors: Dominik Alexander Klein, Dirk Schulz, Simone Frintrop
• Keywords: Computer Vision, ICVS 2009, Joint Feature Pool, Real-time Classification Capabilities, Sensor