Long-term tracking based on spatio-temporal context

被引：1

作者：

Lu J. ^{[1
]}

Chen Y. ^{[1
]}

Zou Y. ^{[1
]}

Zou G. ^{[1
]}

机构：

[1] School of Computer Engineering and Science, Shanghai University, Shanghai

来源：

Journal of Shanghai Jiaotong University (Science) | 2017年 / 22卷 / 04期

关键词：

cascaded search; object detection; object tracking; spatio-temporal context (STC);

D O I：

10.1007/s12204-017-1863-z

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aiming at the problem that the fast tracking algorithm using spatio-temporal context (STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.

引用

页码：504 / 512

页数：8

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