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Modeling uncertainties in performance of object recognition
Efficient probability modeling is indispensable for uncertainty quantification of the recognition data. If the model assumptions do not reflect the intrinsic nature of data and associated random variables, then a strong performance measure will most likely fail to come up with a correct match for recognition. In this paper we propose the probability models for two kinds of data obtained with two distinct goals of recognition: identification and discovery. We consider both frequentisi and Bayesian approaches for drawing inferences from the data
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Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
2.5D multi-view gait recognition based on point cloud registration
This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM
κ΄μ λ€μ€ 보νμ μΆμ μ μν κ³μΈ΅μ κΆ€μ λ§€μΉ κΈ°λ²
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2020. 8. μ΅μ§μ.The purpose of wide-area tracking problem is to track pedestrians that appear on cameras that overlap or do not overlap, regardless of the time interval or person density.
In a single camera tracking, data association using overlapping of the detection boxes is used to solve the tracking problem, but still has appearance ambiguity issues.
However, wide-area tracking requires a tracking scheme that focuses on the appearance similarity of humans, without the use of overlapping of detection boxes.
In this dissertation, we propose the tracking scheme for the Wide-area Multi-Pedestrian Tracking (WaMuPeT).
To achieve the WaMuPeT, we propose the trajectory matching in overlapping camera settings (Ch. 3), non-overlapping camera settings (Ch. 4) and robust trajectory matching in dense scene settings (Ch. 5).
In trajectory matching in overlapping camera settings (Ch. 3), we propose a novel deep-learning architecture for accurate 3-D localization and tracking of a pedestrian using multiple cameras.
The deep-learning network is composed of two networks: detection network and localization network.
The detection network yields the pedestrian detections and the localization network estimates the ground position of a pedestrian within its detection box.
In addition, an attentional pass filter is introduced to effectively connect the two networks.
Using the detection proposals and their 2-D grounding positions obtained from the two networks, multi-camera multi-target 3-D localization and tracking algorithm is developed through min-cost network flow approach.
In the experiments, it is shown that the proposed method improves the performance of 3-D localization and tracking.
In trajectory matching in non-overlapping camera settings (Ch. 4), we propose a novel re-ranking method using a ranking-reflected metric to measure the similarity between two ordered sets of -nearest neighbors (OKNN).
The proposed metric for ranking-reflected similarity (RSS) reflects the ranking of the shared elements between the two OKNNs.
Using RSS, a re-ranking procedure is proposed that prioritizes galleries having neighbors similar to a probe's neighbor in the perspective of ranking order.
In the experiment, we show that the proposed method improves the Re-ID accuracy by add-on to the state-of-the-art methods.
In robust trajectory matching in dense scene settings (Ch. 5), we propose a novel framework for multi-pedestrian tracking to generate robust trajectories in dense scene.
In the proposed tracking method, we propose the tracking method based on the trajectory matching by the strategy of divide and conquer method.
In this strategy, short-term, mid-term and long-term trajectories are generated by each trajectory merging stages, respectively.
Also we propose a novel deep-feature matching method called stable boundary selection (SBS).
In SBS matching, the detections are clustered by the group similarity of deep features, so that robust trajectories can be generated.
With the smoothing algorithms and the detection restoration algorithm, the proposed tracking method shows the state-of-the-art tracking accuracy in three public tracking dataset.κ΄μ μΆμ λ¬Έμ μ λͺ©μ μ μκ° κ°κ²©μ΄λ μ¬λ λ°λμ κ΄κ³μμ΄ κ²ΉμΉκ±°λ κ²ΉμΉμ§ μλ μΉ΄λ©λΌμ λνλλ 보νμλ₯Ό μΆμ νλ κ²μ΄λ€.
λ¨μΌ μΉ΄λ©λΌ μΆμ μμ κ°μ§ μμμ κ²ΉμΉ¨μ μ¬μ©νλ λ°μ΄ν° μ°κ²°μ μΆμ λ¬Έμ λ₯Ό ν΄κ²°νλ λ° μ¬μ©λμ§λ§ μ¬μ ν λͺ¨μ λͺ¨νΈμ± λ¬Έμ κ° μλ€.
κ·Έλ¬λ κ΄μ μΆμ μλ κ°μ§ μμμ κ²ΉμΉ¨μ μ¬μ©νμ§ μκ³ μ¬λμ μΈν μ μ¬μ±μ μ€μ μ λ μΆμ 체κ³κ° νμνλ€.
μ΄ λ
Όλ¬Έμμλ κ΄μ λ€μ€ 보νμ μΆμ (WaMuPeT)μ λν μΆμ 체κ³λ₯Ό μ μνλ€.
WaMuPeTλ₯Ό λ¬μ±νκΈ° μν΄ κ²ΉμΉλ μΉ΄λ©λΌ μ€μ (3 μ₯), κ²ΉμΉμ§ μλ μΉ΄λ©λΌ μ€μ (4 μ₯) μμμ κΆ€μ μΌμΉ κ·Έλ¦¬κ³ λΉ½λΉ½ν μ₯λ©΄ μ€μ (5 μ₯)μμ κ°μΈν κΆ€μ μΌμΉμ λν΄μ μ μνλ€.
κ²ΉμΉλ μΉ΄λ©λΌ μ€μ μμμ κΆ€μ λ§€μΉ (3 μ₯)μμλ μ¬λ¬ μΉ΄λ©λΌλ₯Ό μ¬μ©νμ¬ λ³΄νμλ₯Ό μ ννκ² 3D μ§μννκ³ μΆμ νκΈ°μν μλ‘μ΄ λ₯ λ¬λ μν€ν
μ²λ₯Ό μ μνλ€.
λ₯ λ¬λ λ€νΈμν¬λ κ°μ§ λ€νΈμν¬μ λ‘컬λΌμ΄μ μ΄μ
λ€νΈμν¬μ λ κ°μ§ λ€νΈμν¬λ‘ ꡬμ±λλ€.
νμ§ λ€νΈμν¬λ 보νμ νμ§λ₯Ό μ 곡νκ³ νμ§ν λ€νΈμν¬λ νμ§ μμ λ΄μμ 보νμμ μ§μ μμΉλ₯Ό μΆμ νλ€.
λν λ κ°μ λ€νΈμν¬λ₯Ό ν¨κ³Όμ μΌλ‘ μ°κ²°νκΈ° μν΄μ£Όμ ν¨μ€ νν°κ° λμ
λμλ€.
λ λ€νΈμν¬μμ μ»μ νμ§ μ μ λ° 2D μ μ§ μμΉλ₯Ό μ¬μ©νμ¬ μ΅μ λΉμ©μ λ€νΈμν¬ νλ¦ μ κ·Ό λ°©μμ ν΅ν΄ λ€μ€ μΉ΄λ©λΌ λ€μ€ λμ 3D μ§μν λ° μΆμ μκ³ λ¦¬μ¦μ΄ κ°λ°λλ€.
μ€νμμ μ μ λ λ°©λ²μ΄ 3D μ§μν λ° μΆμ μ±λ₯μ ν₯μμν€λ κ²μΌλ‘ λνλ¬λ€.
κ²ΉμΉμ§ μλ μΉ΄λ©λΌ μ€μ μμμ κΆ€μ μΌμΉ (4 μ₯)μμ, μ°λ¦¬λ μμκ° λ°μλ λ©νΈλ¦μ μ¬μ©νμ¬ λκ°μ μμκ° μ§μ λ -μ΅κ·Ό μ μ΄μ (OKNN) μΈνΈ μ¬μ΄μ μ μ¬μ±μ μΈ‘μ νλ€.
μμ λ°μ μ μ¬μ± (RSS)μ λν΄ μ μ λ λ©νΈλ¦μ λ OKNN μ¬μ΄μ 곡μ μμμ μμλ₯Ό λ°μν©λλ€.
RSSλ₯Ό μ¬μ©νμ¬, μμ μμμ κ΄μ μμ νλ‘λΈμ μ΄μκ³Ό μ μ¬ν μ΄μμ κ°λ κ°€λ¬λ¦¬λ₯Ό μ°μ μμ ννλ μ¬μμ μ μ°¨κ° μ μλλ€.
μ€νμμ μ μ λ λ°©λ²μ΄ μ΅μ λ°©λ²μ μΆκ°λμ΄ Re-ID μ νλκ° ν₯μλ¨μ 보μ¬μ€λ€.
κ³ λ°λ μ₯λ©΄ μ€μ μμ κ°λ ₯ν κΆ€μ μΌμΉ (5 μ₯)μμ, μ°λ¦¬λ κ³ λ°λ μ₯λ©΄μμ κ°λ ₯ν κΆ€μ μ μμ±νκΈ° μν΄ λ€μ€ 보νμ μΆμ μ μν μλ‘μ΄ νλ μ μν¬λ₯Ό μ μνλ€.
μ μλ μΆμ λ°©λ²μμλ λΆν λ° μ 볡 λ°©λ² μ λ΅μ λ°λ₯Έ κΆ€μ 맀μΉμ κΈ°λ°μΌλ‘ μΆμ λ°©λ²μ μ μνλ€.
μ΄ μ λ΅μμ, λ¨κΈ°, μ€κΈ° λ° μ₯κΈ° κΆ€μ μ κ°κ°μ κΆ€μ λ³ν© λ¨κ³μ μν΄ μμ±λλ€.
λν SBS (Stable Boundary Selection)λΌλ μλ‘μ΄ κΈ°λ₯ λ§€μΉ κΈ°λ²μ μ μνλ€.
SBS 맀μΉμμ, νμ§λ κΉμ νΉμ§μ κ·Έλ£Ή μ μ¬μ±μ μν΄ κ΅°μ§νλμ΄, κ°λ ₯ν κΆ€μ μ΄ μμ± λ μ μλ€.
μ μ λ μΆμ λ°©λ²μ νν μκ³ λ¦¬μ¦κ³Ό νμ§ λ³΅μ μκ³ λ¦¬μ¦μ ν΅ν΄ 3 κ°μ κ³΅κ° μΆμ λ°μ΄ν° μΈνΈμμ μ΅μ²¨λ¨ μΆμ μ νλλ₯Ό 보μ¬μ€λ€.Chapter 1 Introduction 1
1.1 Background 1
1.2 Related Works 4
1.2.1 Localization of Pedestrian Detection 4
1.2.2 Pedestrian Feature from Person Re-identification 5
1.2.3 Multi-Pedestrian Tracking 8
1.3 Contributions 8
1.4 Thesis Organization 10
Chapter 2 Problem Statements 11
2.1 Trajectory Matching in Overlapping Camera Settings 11
2.1.1 Challenges 11
2.1.2 Approach for the challenges 13
2.2 Trajectory Matching in Non-Overlapping Camera Settings 13
2.2.1 Challenges 13
2.2.2 Approach for the challenges 14
2.3 Robust Trajectory Matching in Dense Scene Settings 16
2.3.1 Challenges 16
2.3.2 Approach for the challenges 18
Chapter 3 Trajectory Matching in Overlapping Camera Settings 19
3.1 Overall Scheme 19
3.2 Network Design 20
3.3 MCMTT with Proposed Network 22
Chapter 4 Trajectory Matching in Non-overlapping Camera Settings 25
4.1 Overall Scheme 25
4.2 Proposed Method 30
4.2.1 Proposed Similarity Metric 30
4.2.2 Selection of A 31
4.2.3 Re-ranking Procedure 32
Chapter 5 Robust Trajectory Matching in Dense Scene Settings 35
5.1 Overall Scheme 35
5.2 Similarity Matrix Generation 39
5.3 Stable Boundary Selection 40
5.4 Trajectory Smoothing 42
5.5 Detection Restoration 46
5.6 Trajectory Merging Process 48
Chapter 6 Experiments 51
6.1 Dataset and Evaluation Metric 51
6.1.1 Trajectory Matching in Overlapping Camera Settings 51
6.1.2 Trajectory Matching in Non-overlapping Camera Settings 52
6.1.3 Robust Trajectory Matching in Dense Scene Settings 53
6.2 Results and Discussion 56
6.2.1 Trajectory Matching in Overlapping Camera Settings 56
6.2.2 Trajectory Matching in Non-overlapping Camera Settings 56
6.2.3 Robust Trajectory Matching in Dense Scene Settings 62
Chapter 7 Conclusions and Future Works 81
7.1 Concluding Remarks 81
7.2 Future Works 83
Abstract 97Docto
From clothing to identity; manual and automatic soft biometrics
Soft biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of soft biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This study proposes a novel set of soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer-vision. Categorical and comparative soft clothing traits are derived and used for identification/re identification either to supplement soft body traits or to be used alone. The automatically- and manually-derived soft clothing biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications
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