.png){kind=icon}
返回
Object Tracking via Partial Least Squares Analysis
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2012.2205700.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose an object tracking algorithm that learns a set of appearance models for adaptive discriminative object representation. In this paper, object tracking is posed as a binary classification problem in which the correlation of object appearance and class labels from foreground and background is modeled by partial least squares (PLS) analysis, for generating a low-dimensional discriminative feature subspace. As object appearance is temporally correlated and likely to repeat over time, we learn and adapt multiple appearance models with PLS analysis for robust tracking. The proposed algorithm exploits both the ground truth appearance information of the target labeled in the first frame and the image observations obtained online, thereby alleviating the tracking drift problem caused by model update. Experiments on numerous challenging sequences and comparisons to state-of-the-art methods demonstrate favorable performance of the proposed tracking algorithm.
Keyword:
Appearance model
object tracking
partial least squares analysis
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
Increased Bacillus Calmette-Guérin Treatment Intensity Associated With Improved Outcomes in Elderly Patients With Non–Muscle-invasive Bladder Cancer in United States Clinical Practice.png)
Urology
UrologyIF0
PREAI
分享收藏PTX encapsulated by an XG–DOX conjugate for combination therapy against multi-drug resistanceRSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏Operative intervention for primary hyperparathyroidism offers greater bone recovery in patients with sporadic disease than in those with multiple endocrine neoplasia type 1–related hyperparathyroidismSurgery
SurgeryIF0
PREAI
分享收藏
查看更多内容