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