The Strength of Weak Learnability
Machine Learning
An introduction to boosting and leveraging
Advanced lectures on machine learning
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Boosting Nested Cascade Detector for Multi-View Face Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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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.