1,234 research outputs found
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€.
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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
Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 Γ thermal - spatial appearance; 2 Γ optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the LongTerm infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches
Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/
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