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
相关论文
共 50 条
  • [41] Real-Time Deep Learning-Based Object Recognition in Augmented Reality
    Egipko, V
    Zhdanova, M.
    Gapon, N.
    Voronin, V.
    Semenishchev, E.
    REAL-TIME PROCESSING OF IMAGE, DEPTH, AND VIDEO INFORMATION 2024, 2024, 13000
  • [42] Deep Learning-Based Real-time Object Detection in Inland Navigation
    Hammedi, Wided
    Ramirez-Martinez, Metzli
    Brunet, Philippe
    Senouci, Sidi Mohammed
    Messous, Mohamed Ayoub
    2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
  • [43] Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
    Martin-Abadal, Miguel
    Ruiz-Frau, Ana
    Hinz, Hilmar
    Gonzalez-Cid, Yolanda
    SENSORS, 2020, 20 (06)
  • [44] Research on Real-Time Vehicle Detection Algorithm Based on Deep Learning
    Yang, Wei
    Zhang, Ji
    Zhang, Zhongbao
    Wang, Hongyuan
    PATTERN RECOGNITION AND COMPUTER VISION (PRCV 2018), PT IV, 2018, 11259 : 126 - 137
  • [45] Real-time object tracking algorithm based on particle filter and adaptive template update
    Lai, Zuo-Mei
    Chen, Huai-Xin
    Wu, Bi-Fu
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2009, 31 (04): : 768 - 772
  • [46] Integrating shape and color features for adaptive real-time object tracking
    Wang, Junqiu
    Yagi, Yasushi
    2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-3, 2006, : 1 - +
  • [47] Adaptive Features Fusion Correlation Filter for Real-time Object Tracking
    Du, Chenjie
    Gao, Mingyu
    Lan, Mengyang
    Dong, Zhekang
    Yu, Haibin
    He, Zhiwei
    2020 10TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST), 2020, : 120 - 125
  • [48] Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network
    Neupane, Bipul
    Horanont, Teerayut
    Aryal, Jagannath
    SENSORS, 2022, 22 (10)
  • [49] Deepat: a real-time deep learning based model for aircraft tracking system
    Cakici, Muhammed Emir
    Okay, Feyza Yildirim
    Ozdemir, Suat
    JOURNAL OF SUPERCOMPUTING, 2025, 81 (01):
  • [50] A dual-channel correlation filtering tracker for real-time tracking based on deep features of improved CaffeNet and integrated manual features
    Xiao, Yuqi
    Wu, Yongjun
    VISUAL COMPUTER, 2024, : 4347 - 4361
12345