Generic Object Detection Based on Boosting Embedded with Bag-of-Words


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This paper studies generic object detection. In the view of complexity and diversity of generic object, it proposes Boosting generic object detection method with bag-of-words. Boosting method has good detection efficiency, but it has some fault detections due to the diversity and complexity of the object. While Bag-of-words method has some advantages, such as local patch features, simplicity and robustness, and it has good classification performance of complex object. The proposed method applies Bag-of-words to remove the fault detection and to improve the tracking results of Boosting, and thus it achieves high generic object detection accuracy.



Edited by:

Yongping Zhang, Linhua Zhou and Elwin Mao




X. N. Qiu et al., "Generic Object Detection Based on Boosting Embedded with Bag-of-Words", Applied Mechanics and Materials, Vol. 109, pp. 285-289, 2012

Online since:

October 2011




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