群體行為分析是一個跨學科的研究課題.理解群體協作行為的形成機制，是社會科學和自然科學的根本問題之一.群體行為分析的研究可以為很多關鍵的工程應用提供支持和解決方案，比如智能視頻監控系統，人群異常檢測和公共設施優化.在這篇論文中，說們通過研究和分析真實場景中採集的視頻數據，對群體行為提出了有效的計算框架和算法，來分析這視頻中出現的動態群體模式和行為. / 在第一個章節中，我們提出了一個基於馬爾科夫隨機場的圖模型框架，來分析場景中與群體行為相閥的語羲區域. 這個模型利用馬爾科夫隨機場來聯繫行人軌跡的時空關係，可以從高度分散的行人軌跡中進行數據挖掘，以形成完整的群體行為語義區域.其得到的這些語義區域完整地反映出了不同群體行為的進行模式，具有良好的準確性. 這項研究工作已經在IEEE 計算機視覺和模式識別會議(CVPR)2011 發表. / 為了探索語義區域形成的行為學機制，在第二個章節中，我們提出了一個新穎的動態行人代理人混合模型，來分析擁擠場景中出現的人群動態協作行為.每一種行人協作行為模式被建模成一個線性動態系統，行人在場景中的起始和結束位置放建模成這個動態系統的起始和結束狀態. 這個模型可以從高端分散的行人軌跡中分析出共有的協作行為模式。通過模擬行人的行動決策過程，該模型不僅可以分類不同的群體行為，還可以模擬和預測行人的未來可能路徑和目的地.這項研究工作已經在IEEE 計算機視覺和模式織別會議(CVPR) 2012 作為口頭報告發表. / 在第三個章節中，我們首先在協作動態運動中發現了一個先驗定律: 協作領域關係不變性.根據這個先驗定律，我們提出了一個簡單有效的動態聚類技術，稱為協作濾波器.這個動態聚類技術可以運用在多種動態系統中，並且在高密度噪聲下具有很強的魯棒性.在不同視頻中的實驗證明了協作領域關係不變性的存在以及協作濾波器的有效性.這項研究工作已經投稿歐洲計算機視覺會議(ECCV) 2012. / Crowd behavior analysis is an interdisciplinary topic. Understanding the collective crowd behaviors is one of the fundamental problems both in social science and natural science. Research of crowd behavior analysis can lead to a lot of critical applications, such as intelligent video surveillance, crowd abnormal detection, and public facility optimization. In this thesis, we study the crowd behaviors in the real scene videos, propose computational frameworks and techniques to analyze these dynamic patterns of the crowd, and apply them for a lot of visual surveillance applications. / Firstly we proposed Random Field Topic model for learning semantic regions of crowded scenes from highly fragmented trajectories. This model uses the Markov Random Field prior to capture the spatial and temporal dependency between tracklets and uses the source-sink prior to guide the learning of semantic regions. The learned semantic regions well capture the global structures of the scenes in long range with clear semantic interpretation. They are also able to separate different paths at fine scales with good accuracy. This work has been published in IEEE Conference on Computer Vision and PatternRecognition (CVPR) 2011 [70]. / To further explore the behavioral origin of semantic regions in crowded scenes, we proposed Mixture model of Dynamic Pedestrian-Agents to learn the collective dynamics from video sequences in crowded scenes. The collective dynamics of pedestrians are modeled as linear dynamic systems to capture long range moving patterns. Through modeling the beliefs of pedestrians and the missing states of observations, it can be well learned from highly fragmented trajectories caused by frequent tracking failures. By modeling the process of pedestrians making decisions on actions, it can not only classify collective behaviors, but also simulate and predict collective crowd behaviors. This work has been published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012 as Oral [71]. The journal version of this work has been submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). / Moreover, based on a prior defined as Coherent Neighbor Invariance for coherent motions, we proposed a simple and effective dynamic clustering technique called Coherent Filtering for coherent motion detection. This generic technique could be used in various dynamic systems and work robustly under high-density noises. Experiments on different videos shows the existence of Coherent Neighbor Invariance and the effectiveness of our coherent motion detection technique. This work has been published in European Conference on Computer Vision (ECCV) 2012. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhou, Bolei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 67-73). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background of Crowd Behavior Analysis --- p.1 / Chapter 1.2 --- Previous Approaches and Related Works --- p.2 / Chapter 1.2.1 --- Modeling Collective Motion --- p.2 / Chapter 1.2.2 --- Semantic Region Analysis --- p.3 / Chapter 1.2.3 --- Coherent Motion Detection --- p.5 / Chapter 1.3 --- Our Works for Crowd Behavior Analysis --- p.6 / Chapter 2 --- Semantic Region Analysis in Crowded Scenes --- p.9 / Chapter 2.1 --- Introduction of Semantic Regions --- p.9 / Chapter 2.1.1 --- Our approach --- p.11 / Chapter 2.2 --- Random Field Topic Model --- p.12 / Chapter 2.2.1 --- Pairwise MRF --- p.14 / Chapter 2.2.2 --- Forest of randomly spanning trees --- p.15 / Chapter 2.2.3 --- Inference --- p.16 / Chapter 2.2.4 --- Online tracklet prediction --- p.18 / Chapter 2.3 --- Experimental Results --- p.18 / Chapter 2.3.1 --- Learning semantic regions --- p.21 / Chapter 2.3.2 --- Tracklet clustering based on semantic regions --- p.22 / Chapter 2.4 --- Discussion and Summary --- p.24 / Chapter 3 --- Learning Collective Crowd Behaviors in Video --- p.26 / Chapter 3.1 --- Understand Collective Crowd Behaviors --- p.26 / Chapter 3.2 --- Mixture Model of Dynamic Pedestrian-Agents --- p.30 / Chapter 3.2.1 --- Modeling Pedestrian Dynamics --- p.30 / Chapter 3.2.2 --- Modeling Pedestrian Beliefs --- p.31 / Chapter 3.2.3 --- Mixture Model --- p.32 / Chapter 3.2.4 --- Model Learning and Inference --- p.32 / Chapter 3.2.5 --- Algorithms for Model Fitting and Sampling --- p.35 / Chapter 3.3 --- Modeling Pedestrian Timing of Emerging --- p.36 / Chapter 3.4 --- Experiments and Applications --- p.37 / Chapter 3.4.1 --- Model Learning --- p.37 / Chapter 3.4.2 --- Collective Crowd Behavior Simulation --- p.39 / Chapter 3.4.3 --- Collective Behavior Classification --- p.42 / Chapter 3.4.4 --- Behavior Prediction --- p.43 / Chapter 3.5 --- Discussion and Summary --- p.43 / Chapter 4 --- Detecting Coherent Motions from Clutters --- p.45 / Chapter 4.1 --- Coherent Motions in Nature --- p.45 / Chapter 4.2 --- A Prior of Coherent Motion --- p.46 / Chapter 4.2.1 --- Random Dot Kinematogram --- p.47 / Chapter 4.2.2 --- Invariance of Spatiotemporal Relationships --- p.49 / Chapter 4.2.3 --- Invariance of Velocity Correlations --- p.51 / Chapter 4.3 --- A Technique for Coherent Motion Detection --- p.52 / Chapter 4.3.1 --- Algorithm for detecting coherent motions --- p.53 / Chapter 4.3.2 --- Algorithm for associating continuous coherent motion --- p.53 / Chapter 4.4 --- Experimental Results --- p.54 / Chapter 4.4.1 --- Coherent Motion in Synthetic Data --- p.55 / Chapter 4.4.2 --- 3D Motion Segmentation --- p.57 / Chapter 4.4.3 --- Coherent Motions in Crowded Scenes --- p.60 / Chapter 4.4.4 --- Further Analysis of the Algorithm --- p.61 / Chapter 4.5 --- Discussion and Summary --- p.62 / Chapter 5 --- Conclusions --- p.65 / Chapter 5.1 --- Future Works --- p.66
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328644 |
| Date | January 2012 |
| Contributors | Zhou, Bolei., Chinese University of Hong Kong Graduate School. Division of Information Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource (xviii, 73 leaves) : ill. (some col.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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