2 research outputs found
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2019. 2. μ΄κ²½λ¬΄.Person re-identification is a problem of identifying the same individuals among the persons captured from different cameras. It is a challenging problem because the same person captured from non-overlapping cameras usually shows dramatic appearance change due to the viewpoint, pose, and illumination changes. Since it is an essential tool for many surveillance applications, various research directions have been exploredhowever, it is far from being solved.
The goal of this thesis is to solve person re-identification problem under the surveillance system. In particular, we focus on two critical components: designing 1) a better image representation model using human poses and 2) a better training method using hard sample mining. First, we propose a part-aligned representation model which represents an image as the bilinear pooling between appearance and part maps. Since the image similarity is independently calculated from the locations of body parts, it addresses the body part misalignment issue and effectively distinguishes different people by discriminating fine-grained local differences. Second, we propose a stochastic hard sample mining method that exploits class information to generate diverse and hard examples to use for training. It efficiently explores the training samples while avoiding stuck in a small subset of hard samples, thereby effectively training the model. Finally, we propose an integrated system that combines the two approaches, which is benefited from both components. Experimental results show that the proposed method works robustly on five datasets with diverse conditions and its potential extension to the more general conditions.λμΌμΈ νλ³λ¬Έμ λ λ€λ₯Έ μΉ΄λ©λΌλ‘ 촬μλ κ°κ°μ μμμ μ°ν λ μ¬λμ΄ κ°μ μ¬λμΈμ§ μ¬λΆλ₯Ό νλ¨νλ λ¬Έμ μ΄λ€. μ΄λ κ°μμΉ΄λ©λΌμ 보μμ κ΄λ ¨λ λ€μν μμ© λΆμΌμμ μ€μν λκ΅¬λ‘ νμ©λκΈ° λλ¬Έμ μ΅κ·ΌκΉμ§ λ§μ μ°κ΅¬κ° μ΄λ£¨μ΄μ§κ³ μλ€. κ·Έλ¬λ κ°μ μ¬λμ΄λλΌλ μκ°, μ₯μ, 촬μ κ°λ, μ‘°λͺ
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νμκ³ λ³΄λ€ μΌλ°μ μΈ νκ²½μΌλ‘μ νμ₯ κ°λ₯μ±λ νμΈ ν μ μμ κ²μ΄λ€.Abstract i
Contents ii
List of Tables v
List of Figures vii
1. Introduction 1
1.1 Part-Aligned Bilinear Representations . . . . . . . . . . . . . . . . . 3
1.2 Stochastic Class-Based Hard Sample Mining . . . . . . . . . . . . . 4
1.3 Integrated System for Person Re-identification . . . . . . . . . . . . . 5
2. Part-Aligned Bilinear Represenatations 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 Two-Stream Network . . . . . . . . . . . . . . . . . . . . . . 10
2.3.2 Bilinear Pooling . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3.3 Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.1 Part-Aware Image Similarity . . . . . . . . . . . . . . . . . . 13
2.4.2 Relationship to the Baseline Models . . . . . . . . . . . . . . 15
2.4.3 Decomposition of Appearance and Part Maps . . . . . . . . . 15
2.4.4 Part-Alignment Effects on Reducing Misalignment Issue . . . 19
2.5 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . 23
2.6.3 Comparison with the Baselines . . . . . . . . . . . . . . . . . 24
2.6.4 Comparison with State-of-the-Art Methods . . . . . . . . . . 25
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3. Stochastic Class-Based Hard Sample Mining 35
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Deep Metric Learning with Triplet Loss . . . . . . . . . . . . . . . . 40
3.3.1 Triplet Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.2 Efficient Learning with Triplet Loss . . . . . . . . . . . . . . 41
3.4 Batch Construction for Metric Learning . . . . . . . . . . . . . . . . 42
3.4.1 Neighbor Class Mining by Class Signatures . . . . . . . . . . 42
3.4.2 Batch Construction . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.3 Scalable Extension to the Number of Classes . . . . . . . . . 50
3.5 Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.6 Feature Extractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.7 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.7.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.7.2 Implementation Details . . . . . . . . . . . . . . . . . . . . . 55
3.7.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . 56
3.7.4 Effect of the Stochastic Hard Example Mining . . . . . . . . 59
3.7.5 Comparison with the Existing Methods on Image Retrieval
Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70
4. Integrated System for Person Re-identification 71
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2 Hard Positive Mining . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.3 Integrated System for Person Re-identification . . . . . . . . . . . . . 75
4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.4.1 Comparison with the baselines . . . . . . . . . . . . . . . . . 75
4.4.2 Comparison with the existing works . . . . . . . . . . . . . . 80
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.Conclusion 83
5.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Abstract (In Korean) 94Docto