English  |  正體中文  |  简体中文  |  Items with full text/Total items : 70585/70585 (100%)
Visitors : 23099899      Online Users : 375
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84026

    Title: 基於深度學習之關聯式追蹤網路;A novel relational deep network for object tracking
    Authors: 廖浤鈞;Liao, Hung-Chun
    Contributors: 資訊工程學系
    Keywords: 深度學習;物件追蹤;deep learning;object tracking
    Date: 2020-07-28
    Issue Date: 2020-09-02 17:56:52 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 物件追蹤在電腦視覺和深度學習領域是一項熱門的議題,物件追蹤目的在於從一串具連續性的畫面當中找出目標物件的所在位置,現今方法多以深度學習來提高辨識的準確度,而物件追蹤在深度學習領域可分為單物件追蹤及多物件追蹤,前者目的在於判斷物標物件在連續畫面中的位置,而後者目的在於將不同時間點的相同件進行配對,本論文將著重於探討單物件追蹤。
    ;Visual Object Tracking is a popular task in computer vision and deep learning. The purpose of object tracking is to find the location of the target object from a series of continuous images. These years, most object tracking method use deep learning to improve the accuracy. In the field of deep learning, object tracking can be divided into single object tracking and multi-object tracking, the former aims to find the location of the target object in each frames, while the later aims to do the object association, which matches the objects in different time steps. This paper will focus on single object tracking.
    Most of the current deep learning based single object tracking methods use Siamese network architecture, then using the correlation filter to find the correlation between target image and search image. This paper try to improve some existing problems in Siamese based visual object tracking method. We try to add variance loss to enhance the model to distinguish the foreground and the background. Besides, we add the graph convolutional network to improve the accuracy by associating the target object and the surrounding objects.
    Object detection model is to determine whether the target object exists in the image for each input image, but in a continuous series of frames, each frame is slightly different, some objects may be miss detected in some frames, so we try to use tracking model to solve the problem. When the detection model detect the target object, we can use tracking model to track the target in the later frames. We use the visual object tracking model to enhance the stability and the accuracy of the object detection model.
    Appears in Collections:[資訊工程研究所] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明