Online Discriminative Structured Output SVM Learning for Multi-Target Tracking

被引：6

作者：

Xu, Yingkun ^{[1
]}

Qin, Lei ^{[1
]}

Li, Guorong ^{[2
]}

Huang, Qingming ^{[2
]}

机构：

[1] Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China

[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2014年 / 21卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Multi-target tracking; online learning; structured output SVM;

D O I：

10.1109/LSP.2013.2296602

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this letter, we propose an online discriminative learning method for feature combination during multi-target tracking. Previous works utilize offline learned weights for fusion of multiple features, which is not always effective for different tracking contexts. Our work aims to update the weights adaptively in online tracking. We formulate the feature combination problem in data association using structured output SVM, and solve it by online learning algorithm. The constraints of discriminative appearance affinity are integrated to discriminate positive associations from disturbing ones, which makes association more reliable. By comparison with five state-of-the-art methods, our proposed online tracking approach outperforms the other online methods, and is competitive with the global optimal ones.

引用

页码：190 / 194

页数：5