高斯加权的多分类器物体追踪

Gaussian weighted multiple classifiers for object tracking

针对在物体外观快速变化的情况下,大多数弱学习器不能捕获物体新的特征分布,导致追踪失败的问题,提出了高斯加权的联机多分类器增强算法。该算法为每一个领域问题定义一个弱分类器,每个弱分类器包括一个简单的视觉特征和阈值,引入高斯加权函数来权衡每个弱分类器在特定样本上的贡献,通过多分类器联合学习来提高追踪性能。在物体追踪过程中,联机多分类器在对物体定位的同时还能估计物体的姿态,能够成功地学习多模态外观模型,在物体外观快速变化的情况下追踪物体。实验结果表明:所提算法在经过一个较短序列的训练后,平均追踪错误率为12.8%,追踪性能明显提升。

When the appearance of an object changes rapidly, most of the weak learners can not capture the new feature distributions which will lead to tracking failure. In order to deal with that issue, a Gaussian weighted online multiple classifiers algorithm boosting for object tracking was proposed. This algorithm defined one weak classifier which included a simple visual feature and a threshold for each domain problem. Gaussian weighting function was introduced to weigh each weak classifier＇s contribution in a particular sample, therefore the tracking performance was improved through joint learning of multiple classifiers. In the process of object tracking, online multiple classifiers can not only simultaneously determine the location and estimate the pose of the object, but also successfully learn multi-modal appearance models and track an object under rapid appearance changes. The experimental results show that, after a short initial training phase, the average tracking error rate of the proposed algorithm is 12.8%, which proves that the tracking performance has enhanced significantly.