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研究生:郭千豪
研究生(外文):Chien-Hao Kuo
論文名稱:利用骨架資訊實現多重深度攝影機之人物追蹤與行為辨識
論文名稱(外文):People Tracking and Behavior Recognition from Multiple Depth Cameras Using Skeleton Joints
指導教授:張寶基孫士韋
指導教授(外文):Pao-Chi ChangShih-Wei Sun
學位類別:博士
校院名稱:國立中央大學
系所名稱:通訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:95
中文關鍵詞:行為辨識人物追蹤骨架節點多重攝影機深度攝影機監視系統
外文關鍵詞:Behavior recognitionPeople trackingSkeletonJointMultiple depth camerasKinectSurveillance
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人物追蹤與行為辨識已經在娛樂,機器人,監控系統等眾多領域皆發揮關鍵角色的作用。為了使人物追蹤和行為辨識方法得到廣泛地應用,用戶的便利性,安裝的簡易性和設備的合理價格為主要考量的因素。 傳統上,人體運動可用配戴感測器或穿戴型裝置方式捕捉,然而,使用者整天佩帶感測器所造成的額外負擔和使用者可能未配戴感測器,使得此方法較不方便也不牢靠。
另一種方法則是從攝影機獲得的影像進行人物追蹤和識別人類行為。但是,當使用攝影機拍攝二維影像時,影像中人物可能被其他物體所遮蔽,且人物外觀也可能會在三維立體影像中出現多種的可能,深度資訊將會失去,而且結果也可能會受到光影變化的影響。
在本論文中則是利用多重深度攝影機,以克服單一攝影機影像方法的限制,但是遮蔽狀況仍然會降低這種方法的準確度。為了解決這些問題,我們提出了利用來自多重攝像機的人物追蹤軌跡匹配方法用於人物追蹤,使用曲線分群來合成追蹤軌跡。對於行為辨識,我們結合時變基底向量與基於遮蔽權重分配的生成,提出了多重深度相機的時變骨架向量投影的架構。實驗結果顯示,在實際測試環境中與其他最先進的方法相比,本論文提出的方法在人物追蹤失真較少,雖然行為辨識準確度相當,但可更有效地降低計算複雜度。
People tracking and behavior recognition has been emerged to play critical roles in numerous areas including entertainment, robotics, surveillance, etc. In order to make an approach of people tracking and behavior recognition to be widely used, the convenience to users, the simplicity in installation, and the reasonable prices for equipment are the main factors to be considered. The conventional work of capturing human motion is wearing sensors, however, the extra burdens of wearing sensors all of the time and sensors could go unworn, making the task unreliable.
Tracking and recognizing human behavior from images obtained by a monocular camera may be an option. However when taking a 2-D picture of a scene with a monocular camera, the appearance of a person in a 2-D image might pose many possible configurations in 3-D, the depth information will be loose and results could be affect by the lighting conditions. In this dissertation, another solution is concerned with the uses of multiple depth camera to overcome the limitations of the monocular image-based approach, but occlusions still reduce such methods’ accuracy.
To address these problems, we propose a pairwise trajectory matching scheme from multiple cameras for people tracking, using curve clustering to fuse the tracking trajectories. For behavior recognition, a time-variant skeleton vector projection scheme using multiple infrared-based depth cameras is developed by combined proposed time-variant basis vector and proposed occlusion-based weighting element generation. The experiment results shows the proposed method achieves less tracking distortion, superior behavior recognition accuracy and involves less computational complexity compared with other state-of-the-art methods for practical testing environments.
摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 People Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Behavior Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 People Tracking Using Pairwise Trajectory Matching Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Hand-Gesture-Triggered Geometry Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.1 Temporal Synchronization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.2 Relative Hand Joint Issue among Multiple Cameras . . . . . . . . . . . . . . . . . . . . 14
3.2 Proposed People Tracking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2.1 Interleaving-Based Skeletal Joints Obtaining with Valid Skeleton Determination . . . . . 15
3.2.1.1 Interleaving-Based Skeleton Obtaining . . . . . . . . . . . . . . . . . . . . 15
3.2.1.2 Moving Average Based Valid Skeleton Determination . . . . . . . . . . . . . . 16
3.2.2 Multi-Trajectory Matching Using Occlusion Management . . . . . . . . . . . . . . 18
3.2.2.1 Multiple Cameras Projection . . . . . . . . . . . . . . . . . . . . . . 18
3.2.2.2 Occlusion Detection: Multiple Points in One Region . . . . . . . . . . 19
3.2.2.3 Kalman Filter for Multiple-Object Tracking . . . . . . . . . . . . . . 20
3.2.2.4 Pairwise Trajectory Matching . . . . . . . . . . . . . . . . . . . . . 22
3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3.1 Calibration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2 Tracking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.2.1 Performance Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.2.2 Subjective Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3.2.3 Objective Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.2.4 Extensive Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.3 Time Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4 Behavior Recognition Based on a Time-Variant Skeleton Vector Projection . . . . . . . . . . . . . . . . . . . 41
4.1 Relative Joint Position with a Normalization Process . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Basis Vectors Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Projection of Joint Vector Onto the Basis Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.4 Behavior Classifier Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.1 Occlusion-Based Weighting Element Generation . . . . . . . . . . . . . . . . . . . . . . . 51
4.5 Behavior Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.6.1 Quantitative Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.6.2 Qualitative Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.6.3 Complexity Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
7 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
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