计算机工程与应用2019，Vol.55Issue(18)：116-121,139,7.DOI:10.3778/j.issn.1002-8331.1903-0085
结合低维特征和在线加权MIL的目标跟踪算法
Target Tracking Algorithm Based on Low-Dimensional Feature and Online Weighted MIL
摘要
Abstract
In order to improve the accuracy of target tracking in video sequences, a tracking algorithm combining low-dimensional Haar-like feature and Online Weighted Multiple Instance Learning(OWMIL)is proposed. The image in train-ing set is clipped to construct positive and negative sample sets. The low-dimensional Haar-like feature is extracted by sparse coding to represent the target. A weak classifier set is generated by online learning the local sparse features of these positive and negative samples, and an example weighting method is used to promote the learning process. A strong classi-fier is generated for target tracking in test video. Experimental results show that the proposed algorithm achieves excellent results under the influence of rotation, illumination and scale change.关键词
目标跟踪/在线加权多示例学习/Haar-like特征/稀疏表示Key words
target tracking/online weighted multiple instance learning/Haar-like feature/sparse representation分类
信息技术与安全科学引用本文复制引用
孔凡芝,李金龙,吴冬梅..结合低维特征和在线加权MIL的目标跟踪算法[J].计算机工程与应用,2019,55(18):116-121,139,7.基金项目
浙江省科技厅公益项目(No.LGG18F010001) （No.LGG18F010001）
浙江省科技厅公益项目(No.LGG19E050002). （No.LGG19E050002）
