Transferring a generic pedestrian detector towards specific scenes

The performance of a generic pedestrian detector may drop significantly when it is applied to a specific scene due to mismatch between the source dataset used to train the detector and samples in the target scene. In this paper, we investigate how to automatically train a scene-specific pedestrian detector starting with a generic detector in video surveillance without further manually labeling any samples under a novel transfer learning framework. It tackles the problem from three aspects. (1) With a graphical represen-tation and through exploring the indegrees from target sam-ples to source samples, the source samples are properly re-weighted. The indegrees detect the boundary between the distributions of the source dataset and the target dataset. The re-weighted source dataset better matches the target scene. (2) It takes the context information from motions, scene structures and scene geometry as the confidence scores of samples from the target scene to guide trans-fer learning. (3) The confidence scores propagate among samples on a graph according to the underlying visual structures of samples. All these considerations are formu-lated under a single objective function called Confidence-Encoded SVM. At the test stage, only the appearance-based detector is used without the context cues. The effectiveness of the proposed framework is demonstrated through experi-ments on two video surveillance datasets. Compared with a generic pedestrian detector, it significantly improves the de-tection rate by 48 % and 36 % at one false positive per image on the two datasets respectively. 1

Transferring a generic pedestrian detector towards specific scenes.

近年來，在公開的大規模人工標注數據集上訓練通用行人檢測器的方法有了顯著的進步。然而，當通用行人檢測器被應用到一個特定的，未公開過的場景中時，它的性能會不如預期。這是由待檢測的數據（源樣本）與訓練數據（目標樣本）的不匹配，以及新場景中視角、光照、分辨率和背景噪音的變化擾動造成的。在本論文中，我們提出一個新的自動將通用行人檢測器適應到特定場景中的框架。這個框架分為兩個階段。在第一階段，我們探索監控錄像場景中提供的特定表征。利用這些表征，從目標場景中選擇正負樣本並重新訓練行人檢測器，該過程不斷迭代直至收斂。在第二階段，我們提出一個新的機器學習框架，該框架綜合每個樣本的標簽和比重。根據這些比重，源樣本和目標樣本被重新權重，以優化最終的分類器。這兩種方法都屬於半監督學習，僅僅需要非常少的人工干預。使用提出的方法可以顯著提高通用行人檢測器的准確性。實驗顯示，由方法訓練出來的檢測器可以和使用大量手工標注的目標場景數據訓練出來的媲美。與其它解決類似問題的方法比較，該方法同樣好於許多已有方法。本論文的工作已經分別於朲朱朱年和朲朱朲年在杉杅杅杅計算機視覺和模式識別會議（权杖材杒）中發表。In recent years, significant progress has been made in learning generic pedestrian detectors from publicly available manually labeled large scale training datasets. However, when a generic pedestrian detector is applied to a specific, previously undisclosed scene where the testing data (target examples) does not match with the training data (source examples) because of variations of viewpoints, resolutions, illuminations and backgrounds, its accuracy may decrease greatly.In this thesis, a new framework is proposed automatically adapting a pre-trained generic pedestrian detector to a specific traffic scene. The framework is two-phased. In the first phase, scene-specific cues in the video surveillance sequence are explored. Utilizing the multi-cue information, both condent positive and negative examples from the target scene are selected to re-train the detector iteratively. In the second phase, a new machine learning framework is proposed, incorporating not only example labels but also example confidences. Source and target examples are re-weighted according to their confidence, optimizing the performance of the final classifier. Both methods belong to semi-supervised learning and require very little human intervention.The proposed approaches significantly improve the accuracy of the generic pedestrian detector. Their results are comparable with the detector trained using a large number of manually labeled frames from the target scene. Comparison with other existing approaches tackling similar problems shows that the proposed approaches outperform many contemporary methods.The works have been published on the IEEE Conference on Computer Vision and Pattern Recognition in 2011 and 2012, respectively.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Wang, Meng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.Includes bibliographical references (leaves 42-45).Abstracts also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- PedestrianDetection --- p.1Chapter 1.1.1 --- Overview --- p.1Chapter 1.1.2 --- StatisticalLearning --- p.1Chapter 1.1.3 --- ObjectRepresentation --- p.2Chapter 1.1.4 --- SupervisedStatisticalLearninginObjectDetection --- p.3Chapter 1.2 --- PedestrianDetectioninVideoSurveillance --- p.4Chapter 1.2.1 --- ProblemSetting --- p.4Chapter 1.2.2 --- Challenges --- p.4Chapter 1.2.3 --- MotivationsandContributions --- p.5Chapter 1.3 --- RelatedWork --- p.6Chapter 1.4 --- OrganizationsofChapters --- p.9Chapter 2 --- Label Inferring by Multi-Cues --- p.10Chapter 2.1 --- DataSet --- p.10Chapter 2.2 --- Method --- p.12Chapter 2.2.1 --- CondentPositiveExamplesofPedestrians --- p.13Chapter 2.2.2 --- CondentNegativeExamplesfromtheBackground --- p.17Chapter 2.2.3 --- CondentNegativeExamplesfromVehicles --- p.17Chapter 2.2.4 --- FinalSceneSpecicPedestrianDetector --- p.19Chapter 2.3 --- ExperimentResults --- p.20Chapter 3 --- Transferring a Detector by Condence Propagation --- p.24Chapter 3.1 --- Method --- p.25Chapter 3.1.1 --- Overview --- p.25Chapter 3.1.2 --- InitialEstimationofCondenceScores --- p.27Chapter 3.1.3 --- Re-weightingSourceSamples --- p.27Chapter 3.1.4 --- Condence-EncodedSVM --- p.30Chapter 3.2 --- Experiments --- p.33Chapter 3.2.1 --- Datasets --- p.33Chapter 3.2.2 --- ParameterSetting --- p.35Chapter 3.2.3 --- Results --- p.36Chapter 4 --- Conclusions and Future Work --- p.4

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks