||Classification and Retrieval on Human Kinematical Movements
||Department of Computer Science and Information Engineering
Skeleton Discrimination Tree
Human movement retrieval
Mutative Dynamic Programming
||Motion retrieval是一個相當有趣且具挑戰性的研究議題，然而大多數的Motion retrieval系統是建立在2D視訊影像的基礎上。但是隨著3D動作捕捉器影像技術及3D VRML動畫的呈現，使得真實世界中人體運動的軌跡路徑得以利用3D的電腦技術呈現並加以分析及辯識。在本篇論文裡，我們提出一個3D人體運動動作搜尋重現系統，此系統可以使得使用者找出相似的3D人體運動動作，對於動作的分析與比較上，系統主要包含兩個主要的單元，第一個單元是動作類別的分類辨識，其依據的方法是以 Skeleton Discrimination Tree為基礎，Skeleton Discrimination Tree 根據人體運動時，四肢能量的分佈來做動作型態的初步分類，在非跳躍的動作類別型態上的辨識具有明顯的效果，另外可以利用腳部在y軸上能量明顯劇烈的變化以及雙腳是否離開地板等條件來判斷辨識跳躍與非跳躍兩種不同動作群組，此動作類型辨識單元可以過濾掉非相關類別的運動動作。第二個單元包含動作與時間相似度的比較，比較的方法是以mutative dynamic programming演算法為基礎，動作相似度比較是以兩個個別的動作上相對應到的關節點的運動軌跡做比較，我們詢問了數位體育界教授的意見及參考了許多文獻資料，根據教授們的意見及文獻資料訂定了人體上16個做為軌跡追蹤的重要關節點，包括手腕、手肘、腳踝、膝蓋、頸部、頭及其他重要的人體關節點等，做為比較的軌跡都是由這16重要特徵關節點所擷取出，每一段軌跡都是由連續的點座標所組成的，這些連續的點座標會被轉換成並以連續的向量方式呈現，我們就以不同空間中的兩個相對應軌跡上的向量所形成的夾角大小程度來判斷是否是屬於一樣的序列元素，以mutative dynamic programming求出兩軌跡路徑的最長共同序列，有愈長的共同序列表示動作相似度愈高。求出兩軌跡路徑的最長共同序列後，我們進一步計算出最長共同序列相對應元素的時間差異度，避免系統將雙手同時舉起與雙手沒有同時間舉起的動作誤判為一樣的動作。
||Motion retrieval is a quite interesting but challenging research topic. However, for the most motion retrieval systems are designed based on 2-D video information. But, with the motion capture of video technology and presented by VRML animation, it is possible to automatically represent, analyze and adjust the 3D motions of real person. In this study, we propose a 3D human movement retrieval system, which allows users to retrieve 3D kinematical movements. The system includes two major components for movement analysis and comparison. The first one is a recognition unit of movement types which is based on Skeleton Discrimination Tree. The Skeleton Discrimination Tree can judge movement types in the field of Un-Jump. According to the conceptions of violent variation of energy of foots in Y axis and the feature information that if both foots are stuck on the ground, we can distinguish the movement types which belong to groups of “Jump” or “Un-Jump”. This recognition component can filter unallied human movements. The second unit includes movement and synchronization similarity. The comparing approach is based on mutative dynamic programming that considers the degree of the included angles of the vectors which belong to individual feature tracks. There are 16 track points include head, knee, elbow, wrist, etc. and further aggregate important features of human joints. The trajectories that can be used to comparison are extracted from these 16 feature joints. Each trajectory is composed of serial coordinates. The serial coordinates would be transformed and represented as successive vectors. We use the succession of vectors as the feature information to compute the similarity based on mutative Dynamic Programming.
The forms of the feature coordinate points are variational numeral data. The variational numeral data can not provide satisfied for human sense of sight. Furthermore, only based on the variational numeral data, it is difficult to experience the motion variation of whole human body parts with a spatial-template domain. To solve this problem, the feature coordinates points of human body parts’ trajectories are transformed into a 3-D human body model as VRML animations. Users may give a VRML human movement object, and find the similar human movements via the system. As a result, the system can automatically retrieve similar actions. The results are tested by three professors of physical education and master students with a good satisfaction. Besides, our system provides adaptive parameter which dynamically calculated according to user’s perception of motion features. The query object also can compare with standard human kinematical motion to find the difference in each joint.
||LIST OF FIGURES III
LIST OF TABLES V
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 APPLICATION OF 3D MOTION RETRIEVAL 2
1.3 OVERVIEW OF APPROACH TAKEN 3
1.3.1 PRE-WORKS 4
1.3.2 MAIN WORKS 5
CHAPTER 2 RELATED WORK 9
2.1 TRACKING 9
2.1.1 MODEL-BASED TRACKING 10
2.1.2 FEATURE-BASED TRACKING 14
2.1.3 MULTI-CAMERA TRACKING 16
2.2 BEHAVIOR UNDERSTANDING 16
2.2.1 GENERAL TECHNIQUES 17
2.2.2 ACTION RECOGNITION 20
CHAPTER 3 SYSTEM REQUIREMENT 25
3.1 VRML 25
3.1.1 A WORD ABOUT VRML VERSIONS 26
3.1.2 THE VRML SYNTAX 29
3.2 3D BROWSER 35
3.4 CORTONA SDK 40
CHAPTER 4 THE PROPOSED SCHEMES 42
4.1 REPRESENTATION OF SKELETION 43
4.2 FEATURE SPACE 44
4.2.1 FEATURE EXTRACTION 44
4.2.2 RELATIVE COORDINATES TRANSFORMED INTO ABSOLUTE COORDINATES 44
4.2.3 FEATURE REPRESENTATION 46
4.3 CLASSIFICATION OF MOVEMENT TYPES 48
4.3.1 SKELETON DISCRIMINATION TREE 48
4.4 SIMILARITY OF HUMAN MOVEMENT 51
4.4.1 HUMAN MOTION SIMILARITY 52
4.4.2 THE SIMILARITY OF SYNCHRONIZATION 57
4.5 IMPROVING THE SPORT SKILL 60
4.6 GRAPHIC USER INTERFACE 63
CHAPTER 5 EXPERIMENT AND ANALYSIS 64
5.1 ANALYSIS OF DYNAMIC PROGRAMMING 64
5.2 EXPERIMENT AND COMPARISON 67
CHAPTER 6 CONCLUSION AND FUTURE WORK 71
PAPER LIST 86
LIST OF FIGURES III
Figure 1. 3D animation in VRML 31
Figure 2. The VRML plugin--cortona plug-ins in Internet Explorer which shows the VR scene 37
Figure 3. (A) and (B) are motion capture, (C) VICON system and (D) initialize state in filming procedure, A subject wearing a retro-reflective marker set in the NCPES Motion Capture Laboratory. 38
Figure 4. Representation of 3D VRML human motion models (A) Standing Board Jump. (B) Throwing Act 40
Figure 5. Overview of system architecture 42
Figure 6. A Human Body Skeleton 44
Figure 7. Feature Points of a Body Skeleton and Animation Tracks 45
Figure 8. Skeleton Discrimination Tree 50
Figure 9. Example of query trajectory 55
Figure 10.The result generated by the Fine-Tuning -Angle algorithm 56
Figure 11.The result generated by our proposed Rough-Tuning-Angle algorithm 56
Figure 12.Differences between standard kinematical movement and query object in x,y,z 61
Figure 13.The GUI shows the difference of 8 joints between the standard human movement and query object 62
Figure 14.User interface of a 3D kinematical movement retrieval system. 63
Figure 15.The motions have the same track, but with different timer 64
Figure 16.The motions have the same track, but with different timer 65
Figure 17.(A) Query object (B) The result generated by the method applied to “Min” function 66
Figure 18.The results generated by our proposed DP method applied to “Max” function 67
Figure 19.The results of our system.(A) Baseball Swing with right hand, (B) Side Baseball Pitching with left hand 69
LIST OF TABLES
Table 1. Overview of VRML versions 27
Table 2. A VRML file sample 32
Table 3. Overview of 3D Browser Plugins 36
Table 4. Timer of a motion sequence 46
Table 5. Parameters for Similarity Calculation 50
Table 6. Correspondence relation between qh and th, and they have the same longest common sequence such as q’h and t’h. 58
Table 7. The experiential result of DP approach applied to “Max” and “Min” 66
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