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系統識別號 U0026-0812200913595956
論文名稱(中文) 結合粒子濾波器與多向運動學模型進行人類部份肢體追蹤
論文名稱(英文) Particle Filter based Human Body Parts Tracking with Multi-directional Kinematic Models
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 95
學期 2
出版年 96
研究生(中文) 徐振益
研究生(英文) Chen-Yi Hsu
學號 q3694125
學位類別 碩士
語文別 英文
論文頁數 38頁
口試委員 口試委員-張建禕
口試委員-黃春融
口試委員-王駿發
指導教授-詹寶珠
口試委員-鍾翼能
中文關鍵字 粒子濾波器  形狀上下文 
英文關鍵字 shape context  particle filter 
學科別分類
中文摘要 人類肢體追蹤在電腦視覺領域扮演著相當重要的角色。本篇論文結合了區域的形狀上下文比較與粒子濾波器追蹤和全域的多向運動學模型來追蹤人類肢體。ㄧ個輸入的影片, 經由背景提取法分別切割出移動物體和前景輪廓像。在初始化時, 我們會在移動物體和前景輪廓圖上圈出三個主要身體部份。然後使用形狀上下文分別在移動物體和前景輪廓圖上表示每個身體部份。在使用粒子濾波器追蹤時, 我們發現擁有最大事後機率的位置不一定是最接近身體部位的位置。所以我們使用多向用動學模型來使我們的準確率提高。為了提高強健度,我們還會估算出人體骨架藉以追蹤膝蓋與腳踝。我們的實驗結果經過量化分析後顯示我們的方法有不錯的效果。
英文摘要 Human body parts tracking plays an important role in computer vision domain. This paper combines local shape context matching with particle filters and using a global constrain of multi-directional kinematic models to track human body parts. Given an input video, the moving object (MO) and its foreground silhouette (FS) are obtained separately by using background subtractions. During the initialization, three main body parts are assigned for both MO and FS first. Then, shape contexts are used to represent for each body part of MO and FS. While applying particle filtering, we found that the position of maximum posterior probability may not be the fittest body parts. By leading in the idea of human kinematics model, the tracked would be more accuracy. To increase the robustness, the skeleton approximating would be applied to track the position of knee and ankle furthermore. Our quantitative analysis of experimental results showed a good performance by using our proposed method.
論文目次 1 INTRODUCTION 1
2 RELATED WORKS 3
2.1 1D-Model Showing Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 2D-Model Representation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3 3D-Model Recovering Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4 Descriptor/Feature-based Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4.1 Generalized Feature Domain . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4.2 Innovation through Local Descriptors . . . . . . . . . . . . . . . . . . . . . 6
2.4.3 Our Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 PROPOSED ARCHITECTURE 8
3.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Shape Context Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Particle Filter Tracking with Multi-directional Kinematic Models . . . . . . . . . . . 12
3.3.1 Human Kinematics Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.2 Predictive Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.3 Multi-directional Kinematic Models . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Skeleton Approximating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4.1 Locating Angle Positions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4.2 Inverse Kinematic Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 EXPERIMENTS AND PERFORMANCE EVALUATION 20
4.1 Parameter Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2.1 Comparisons . . . . . . . . . . .27
5 CONCLUSIONS 31
Bibliography 32
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