학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 최진영.A number of surveillance cameras have been installed for safety and security in actual environments. To achieve a human-level visual intelligence via cameras, there has been much effort to develop many computer vision algorithms realizing the various visual functions from low level to high level. Among them, the moving object detection is a fundamental function because the attention to a moving object is essential to understand its high-level behavior. Most of moving object detection algorithms in a fixed camera adopt the background-centric modeling approach. However, the background-centric approach does not work well in a moving camera because the modeling of moving background in an online way is challengeable. Until now, most algorithms for the object detection in a moving camera have relied on the object-centric approach using appearance-based recognition schemes. However, the object-centric approach suffers from the heavy computational complexity. In this thesis, we propose an efficient and robust scheme based on the background-centric approach to detect moving objects in the dynamic background environments using moving cameras. To tackle the challenges arising from the dynamic background, in this thesis, we deal with four problems: false positives from inaccurate camera motion estimation, sudden scene changes such as illumination, slow moving object relative to camera movement, and motion model limitation in a dashcam video.
To solve the false positives due to motion estimation error, we propose a new scheme to improve the robustness of moving object detection in a moving camera. To lessen the influence of background motion, we adopt a dual-mode kernel model that builds two background models using a grid-based modeling. In addition, to reduce the false detections and the missing of true objects, we introduce an attentional sampling scheme based on spatio-temporal properties of moving objects. From the spatio-temporal properties, we build a foreground probability map and generate a sampling map which selects the candidate pixels to find the actual objects. We apply the background subtraction and model update with attention to only the selected pixels.
To resolve sudden scene changes and slow moving object problems, we propose a situation-aware background learning method that handles dynamic scenes for moving object detection in a moving camera. We suggest new modules that utilizes situation variables and builds a background model adaptively. Our method compensates for camera movement and updates the background model according to the situation variables. The situation-aware scheme enables the algorithm to build a clear background model without contamination by the foreground.
To overcome the limitation of motion model in a dashcam video, we propose a prior-based attentional update scheme to handle dynamic scene changes. Motivated by the center-focused and structure-focused tendencies of human attention, we extend the compensation-based method that focuses on the center changes and neglects minor changes on the important scene structure. The center-focused tendency is implemented by increasing the learning rate of the boundary region through the multiplication of the attention map and the age model. The structure-focused tendency is used to build a robust background model through the model selection after the road and sky region are estimated.
In experiments, the proposed framework shows its efficiency and robustness through qualitative and quantitative comparison evaluation with the state-of-the arts. Through the first scheme, it takes only 4.8 ms in one frame processing without parallel processing. The second scheme enables to adapt rapidly changing scenes while maintaining the performance and speed. Through the third scheme for the driving situation, successful results are shown in background modeling and moving object detection in dashcam videos.1 Introduction 1
1.1 Background 1
1.2 Related works 4
1.3 Contributions 10
1.4 Contents of Thesis 11
2 Problem Statements 13
2.1 Background-centric approach for a fixed camera 13
2.2 Problem statements for a moving camera 17
3 Dual modeling with Attentional Sampling 25
3.1 Dual-mode modeling for a moving camera 26
3.1.1 Age model for adaptive learning rate 28
3.1.2 Grid-based modeling 29
3.1.3 Dual-mode kernel modeling 32
3.1.4 Motion compensation by mixing models 35
3.2 Dual-mode modeling with Attentional sampling 36
3.2.1 Foreground probability map based on occurrence 37
3.2.2 Sampling Map Generation 41
3.2.3 Model update with sampling map 43
3.2.4 Probabilistic Foreground Decision 44
3.3 Benefits 45
4 Situation-aware Background Learning 47
4.1 Situation Variable Estimation 51
4.1.1 Background Motion Estimation 51
4.1.2 Foreground Motion Estimation 52
4.1.3 Illumination Change Estimation 53
4.2 Situation-Aware Background Learning 54
4.2.1 Situation-Aware Warping of the Background Model 54
4.2.2 Situation-Aware Update of the Background Model 55
4.3 Foreground Decision 58
4.4 Benefits 59
5 Prior-based Attentional Update for dashcam video 61
5.1 Camera Motion Estimation 65
5.2 Road and Sky region estimation 66
5.3 Background learning 69
5.4 Foreground Result Combining 75
5.5 Benefits 77
6 Experiments 79
6.1 Qualitative Comparisons 82
6.1.1 Dual modeling with attentional sampling 82
6.1.2 Situation-aware background learning 84
6.1.3 Prior-based attentional update 88
6.2 Quantitative Comparisons 91
6.2.1 Dual modeling with attentional sampling 91
6.2.2 Situation-aware background learning 91
6.2.3 Prior-based attentional update (PBAU) 93
6.2.4 Runtime evaluation 94
6.2.5 Unified framework 94
6.3 Application: combining with recognition algorithm 98
6.4 Discussion 102
6.4.1 Issues 102
6.4.2 Strength 104
6.4.3 Limitation 105
7 Concluding remarks and Future works 109
Bibliography 113
초록 125Docto
