FLOATING-BAGGING-ADABOOST ENSEMBLE FOR OBJECT DETECTION USING LOCAL SHAPE-BASED FEATURES

摘要：

We propose a novel learning algorithm, called Bagging-Adaboost ensemble algorithm with floating search algorithm post optimization, for object detection that uses local shape-based feature. The feature use the chamfer distance as a shape comparison measure. It can be calculated very quickly using a look-up table. Random sampling boosting algorithm is used to form an object detector. Floating search post optimization procedure is used to remove base classifiers which cause higher error rates. The resulting classifier consists of fewer base classifiers yet achieves better generalization performance. To demonstrate our method we trained a system to detect pedestrians in complex natural scenes. Experimental results show that our system can extremely rapidly detect objects with high detection rate. The learning techniques can be extended to detect other objects.

关键词： Feature selection Pattern recognition Machine learning Shape feature Computer vision

作者: XU-SHENG TANG ZHE-LIN SHI DE-QIANG LI LONG MA DAN CHEN

作者单位: Shenyang Institution of Automation, the Chinese Academy of Sciences, Shenyang, 110016, China

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 45-49

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）