An Improved Real-Time Object Tracking Algorithm Based on Deep Learning Features

被引：0
|
作者：
Wang, Xianyu [1 ,2 ]
LI, Cong [2 ]
LI, Heyi [3 ]
Zhang, Rui [4 ]
Liang, Zhifeng [4 ]
Wang, Hai [3 ]
机构：
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Acad Space Elect Informat Technol, Xian 710100, Peoples R China
[3] Xidian Univ, Sch Aerosp Sci & Technol, Xian 710071, Peoples R China
[4] Shaanxi Aerosp Technol Applicat Res Inst Co Ltd, Xian 710100, Peoples R China
来源：
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2023年 / E106D卷 / 05期
关键词：
object tracking; feature fusion; deep learning; model update; re-detection;
D O I：
10.1587/transinf.2022DLP0039
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Visual object tracking is always a challenging task in computer vision. During the tracking, the shape and appearance of the target may change greatly, and because of the lack of sufficient training samples, most of the online learning tracking algorithms will have performance bottlenecks. In this paper, an improved real-time algorithm based on deep learning features is proposed, which combines multi-feature fusion, multi-scale estimation, adaptive updating of target model and re-detection after target loss. The effectiveness and advantages of the proposed algorithm are proved by a large number of comparative experiments with other excellent algorithms on large benchmark datasets.
引用
收藏
页码：786 / 793
页数：8
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