Paper Info
Title
Boosting with a Joint Feature Pool from Different Sensors
Abstract
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. We found that for all object categories the classification performance is considerably improved by the joint feature pool.
Year
DOI
Venue
2009
10.1007/978-3-642-04667-4_7
ICVS
Keywords
Field
DocType
haar-like feature,different sensor,system performance,single sensor,different object category,joint feature pool,different sensors,sensor layer,object category,object type dependent color,different sensor data,spatial correlation,mobile robot,distance metric,color vision,real time
Object type,Computer science,Hue,Metric (mathematics),Artificial intelligence,Color vision,Computer vision,Spatial correlation,Pattern recognition,Sensor fusion,Boosting (machine learning),Mobile robot,Machine learning
Conference
Volume
ISSN
Citations 
5815
0302-9743
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
Dominik Alexander Klein1855.46
Dirk Schulz21701236.54
Simone Frintrop369542.88