變動複雜背景下物體追蹤之研究

A Study of Object Tracking in Varying Complex Backgrounds

卓柏全(Po-Chuan Cho)

指導教授 ： 陳文輝

國立臺北科技大學/機電學院/自動化科技研究所/碩士(2009年)

https://doi.org/10.6841/NTUT.2009.00511

摘要

本文探討在複雜背景環境下移動物體追蹤的問題，並以游泳池為例，至現場實際拍攝的影片驗證我們所發展追蹤模型的正確性。系統架構分為移動物體偵測與追蹤兩個部份。首先以平均位移演算法對輸入影像做聚類分析，再利用圖形機率模型選出合適的訓練樣本，作為建立高斯混合模型的基礎，據以偵測出移動物體的正確位置。接著，比較平均移動、卡爾曼濾波器和粒子濾波器三種常用的物體追蹤演算法，對偵測階段所鎖定的對象進行追蹤準確度的探討。由實驗結果顯示本文所提出之方法，可以有效追蹤變動背景下移動物體之移動軌跡。

關鍵字

機器學習 ； 平均位移 ； 卡爾曼濾波器 ； 粒子濾波器

並列摘要

An object tracking algorithm in a varying complex background, swimming pool, has been studied in this thesis. And its accuracy has been verified by video clips. The proposed approach consists of two main stages: swimmer detection and swimmer tracking. The detection stage begins with employing the mean-shift algorithm to cluster input image, and then chooses the sets by graphical models to train the Gaussian mixture model. Finally, swimmers can be detected in a model-based way. The main purpose in this tracking stage is to compare the accuracy among Kalman filter, mean-shift tracking algorithm, and particle filter algorithms. The experimental results show that the proposed approach can track swimmers efficiently. In the future, we hope to apply it on drowning alarm systems in swimming pools.

並列關鍵字

Machine learning ； Mean-shift ； Kalman filter ； Particle filter

參考文獻

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