Online Object Tracking: A Benchmark

被引：3116

作者：

Wu, Yi ^{[1
]}

Lim, Jongwoo ^{[2
]}

Yang, Ming-Hsuan ^{[1
]}

机构：

[1] Univ Calif Merced, Merced, CA 95343 USA

[2] Hanyang Univ, Seoul, South Korea

来源：

2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2013年

关键词：

ROBUST; MODELS;

D O I：

10.1109/CVPR.2013.312

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.

引用

页码：2411 / 2418

页数：8