人走路的姿態，可能會受到疾病、生理狀況的影響而有所改變。像是有肺功能障礙的病人在走路時會有很大的喘氣，太過於疲勞的人走路的步調會非常緩慢而漸趨向一致。人走路的行為不僅僅可以用在步態分析上，去偵測病人的狀況，也可以用在人的辨識上，在輕鬆走路的狀態下，每個人走路都有自己的習慣與特色像是步伐的快慢、擺手的高度、抬腳的高度、身體是否有駝背…等情況。
我們根據影像上人體的幾何關係提出人體切割的演算法，去建立人身體各部分的模組像是頭，上半身、手、腳。接下來利用各部分的重心去建立角度、比值、步伐的特徵點。在步態的分析，我們設計不同的實驗，分別對同一個人在有呼吸有阻塞、有駝背和一般時做的分析。當呼吸阻塞時會有很產生很大喘氣，我們觀察出上半身與頭的角度會比一般正常走路時大將近2.49倍，而有駝背的情況，會比正常的情況大5.5倍左右。另一個關於疲勞程度的分析，我們測量到當人很疲勞時，他們的步伐變化率會低於3。因為他們的精神狀況較差，沒有辦法像一般走路時消耗那麼多的能量，所以會以人消耗最少能量的方式去行走，而導致步伐漸漸緩慢而趨近一致。最後一個實驗是關於速度，我們要求測試者用自己所認知的快速度與慢速度去分別測試，可以知道快的速度會比慢的速度快大約1.347倍。這些實驗，我們可以把一些病態的、生理狀況差的情況分辨出來，當所測量到的特徵點數值大於或小於某個臨界值時，我們就可以告知警訊。
我們的方法除了用在分析步態上，我們還把我們提出的特徵點去做人的辨識。我們測試兩個標準的資料庫，每筆資料庫擁有100個以上不同的人走路的影像。我們在一般的情況下使用相同的測試與訓練去做辨識，可以達到90%以上的辨識率，可以作為我們辨識參考的依據。雖然結果，與無模組的方法比不好，但是可以顯示出我們的方法比較適合用在分析走路姿態，因為我們在不同環境狀況變化下，都可以分辨出不同。正常狀況下走路，我們提出方法與模組的方法做比較，辨識率可以高達96.97%，可以作為與模組方法辨識參考的依據。

Human walking behaviour can be affected by the disorders, physiological condition such as the chronic obstructive pulmonary disease(COPD) patient walking with large breathing, the extremely fatigue people walking in static pace rhyme. The walking behaviour can not only be used in gait analysis to detect the patient condition, but also be used in gait recognition. Everyone has his own characteristics and habits under the comfortable walking status such as the pace velocity, the waving of arms, the lifting of legs, the humpbacked and else.

We base on the geometry of human to segment the silhouette on image processing in order to construct the model of body parts such as head, upper torso, lower torso, arms, and legs. We utilize the mass of body parts to develop our proposed features of angles, ratios, and pace. In gait analysis, we design different experiments for the people with respiratory obstruction, with bending and in normal. When people with respiratory obstruction, they have large breathing because of lacking oxygen. Also, the angle of head, and upper torso has the $2.49$ times larger than normal. In humpbacked condition, the angle is $5.5$ times as large as the normal condition. Other fatigue detection experiment, we observe that people in extremely fatigue condition has the pace rhyme lower than $3$ because the pace becomes static and slow. It is because that people have tired physiological condition and they can not walk like normal walking with energy. As a result, people walk with the lowest power manner to reduce their energy consuming. In the velocity experiment, we request the people to walk fast and slow, and the velocity of fast case is $1.347$ times as large as the slow normal walking condition. Taking these above experiments into consideration, we can discriminate some symptoms of patient, the poor physiological condition. When the detected features are larger or lower than the threshold which depends on the normal condition, we can send a notice.

We not only utilize our features on gait analysis, but also on human gait recognition. We experiment on the two benchmark databases which both have the walking images above 100 different people. Our recognition rate on normal condition can both achieve $90\%$ under same testing and training database as a reference. Although, our recognition rate is not better than model-free approaches, it can display that our proposed method can discriminative the people under different environments when walking. The experiment comparing with model-based method under normal walking status has highest performance with $96.97\%$ accuracy using CASIA database B, and we could use it as a reference for gait recognition rate to compare with other model-based methods.

1 Introduction 1
1.1 Motivation 1
1.2 Problem Description 3
1.2.1 Gait Analysis 3
1.2.2 Gait Recognition 5
1.2.3 Gait Analysis and Gait Recognition 5
1.3 Goal and Contribution 6
1.4 Thesis Organization 8
2 Related Works 9
2.1 Gait Analysis 9
2.2 Gait Recognition 10
2.3 Summary 12
3 Feature Extraction 14
3.1 Pre-processing 16
3.2 Segmentation 17
3.3 Feature Expression 22
3.3.1 Angle 23
3.3.2 Ratio 25
3.3.3 Pace 26
4 Feature Analysis 28
4.1 Gait Analysis 28
4.1.1 Analysis 28
4.1.1.1 Angles on upper body 30
4.1.1.2 Angles on limbs 31
4.1.1.3 Pace 32
4.1.2 Grading 33
4.2 Gait Recognition 35
4.3 Nodding Detection 38
5 Experimental Results 41
5.1 Gait Analysis 41
5.1.1 The Characteristics of Angles when Walking 41
5.1.2 The Characteristics of Physiological Condition 44
5.1.3 The Velocity of Mutual Comparison 46
5.1.4 The Proposed Application on gait analysis 48
5.2 Gait Recognition 49
5.2.1 Database Description 50
5.2.1.1 CASIA Database B: Wearing Style Variation 50
5.2.1.2 CASIA Database C: Speed Variation 51
5.2.2 Comparison on Model-Free Gait Recognition 51
5.2.2.1 CASIA Database B 51
5.2.2.2 CASIA Database C 53
5.2.3 Comparison on Model-Based Gait Recognition 54
5.3 Nodding detection 55
6 Conclusions 58
Reference 59

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