中大機構典藏-NCU Institutional Repository-提供博碩士論文、考古題、期刊論文、研究計畫等下載:Item 987654321/89847
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 80990/80990 (100%)
Visitors : 42142358      Online Users : 1033
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/987654321/89847


    Title: 基於物件偵測之多物件追蹤關聯策略;A Novel Matching Strategy of Detection-Based Multi-Object Tracking
    Authors: 張維珊;Chang, Wei-Shan
    Contributors: 資訊工程學系
    Keywords: 多物件追蹤;行人追蹤;外觀特徵;資料關聯;卡爾曼濾波器;Multiple-Object Tracking;Appearance Similarity;Data Association
    Date: 2022-07-29
    Issue Date: 2022-10-04 12:01:56 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 多物件追蹤技術應用相當廣泛,不論是從監視器畫面進行車流監控、人流監控、行人追蹤、球場上球員走位的戰術分析都會用到多物件追蹤的技術,其主要任務是將一段影片中分布在不同幀的偵測框正確的關聯起來,困難的地方在於當目標被長時間遮蔽、消失、場景複雜的情況下容易發生追蹤錯誤的情況,雖然已有許多研究提出不同的追蹤策略來解決此問題,但追蹤結果仍有可進步的空間。
    為了提高多物件追蹤的準確度,本論文基於ByteTrack架構上提出一個兩階段的Online多物件追蹤方法: MEDIATrack,我們將Kalman Filter更換為NSA Kalman Filter、引入外觀特徵作為資料關聯參考資訊,並設計懲罰機制去緩解在場景複雜所出現的錯誤情況,此外也移除歷史軌跡中未激活軌跡的機制,直接將高信心值未匹配上的偵測框新增至歷史軌跡,使得本研究在MOT17資料集達到79.3(%) MOTA,達到了state-of-the-art的水準。
    ;Multi-object tracking (MOT) is widely applied to traffic flow monitoring, human flow monitoring, pedestrian tracking, or tactical analysis of players on the courts. It associates the detection boxes with tracklets for each frame in the video. The challenges of MOT include long-term occlusions, missing detections, and complex scenes. Although many trackers have proposed to solve these problems, the tracking results still have room for improvement. In this thesis, we propose a solution named MEDIATrack, which is a two-stage online multi-object tracking method based on the ByteTrack. We replace the Kalman Filter with the NSA Kalman Filter, introduce appearance features for track association, and design a punishment mechanism to alleviate errors in complex scenes. In addition, we remove the nonactivated strategy, and the high-score unmatched detection boxes are directly added to the tracklets. On MOT17, we achieve 79.3 MOT Accuracy and state-of-the-art performance.
    Appears in Collections:[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML42View/Open


    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 ©   - 隱私權政策聲明