点击次数：

论文类型：会议论文

发表时间：2011-03-21

收录刊物：EI、Scopus

页面范围：545-552

摘要：We propose an online tracking algorithm in which the support instances are selected adaptively within the multiple instance learning framework. The support instances are selected from training 1-norm support vector machines in a feature space, thereby learning large margin classifiers for visual tracking. An algorithm is presented to update the support instances by taking image data obtained previously and recently into account. In addition, a forgetting factor is introduced to weigh the contribution of support instances obtained at different time stamps. Experimental results demonstrate that our tracking algorithm is robust in handling occlusion, abrupt motion and illumination. ? 2011 IEEE.