Boosting based object detection using a geometric model
| Autor: | Andre Kaup, Mohan M. Trivedi, Katharina Quast, Christoph Seeger |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Boosting (machine learning)
Computer science business.industry Detector Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition Scale-invariant feature transform Pattern recognition Boosting methods for object categorization Object detection Computer vision Artificial intelligence business Geometric modeling Classifier (UML) |
| Zdroj: | ICIP |
| Popis: | In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular AdaBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated in a voting space. The voting space allows a detection with combinations of different object features. We trained and tested the proposed method with SIFT and kAS features and combinations of these. The learned detector is then used to localize objects in images and video sequences. The performance of the algorithm is examined based on selected image data. |
| Databáze: | OpenAIRE |
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