Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

An Examination of Railroad Capacity and its Implications for Rail-Highway Intermodal Transportation

After many years of decline in market share, railroads are now experiencing an increasing demand for their services. Service intensive intermodal transportation seems to be an especially promising market area. Since the historic decline in traffic has been accompanied by a reduction in network infrastructure, however, the railroads\u27 ability to handle sizable traffic increases, at least in the short term, is in question. Since rail transportation is critical to the domestic economy of the nation, and is increasingly important in international logistics channels, shortfalls in railroad capacity are not desirable. The published literature on railroad capacity is relatively sparse, especially in comparison to the highway mode. Much of what is available pertains to individual network components such as lines or terminals. Evaluation of system capacity, considering the interactive effects of traffic flowing through a network of lines and terminals, has received less attention. A tool specifically designed for evaluating freight railroad system capacity issues could be a useful addition to the rail analyst\u27s toolbox. The research conducted in this study resulted in the formulation and application of RAILNET, a multicomrnodity, multicarrier network model for predicting equilibrium flows within a railroad network. Designed for strategic planning with a short term horizon, the model assumes fixed external demand. The predicted flows meet the conditions for Wardropian system equilibrium. At completion, the solution algorithm predicts the expected delay per train on each link, allowing the analyst to identify areas of congestion. Following completion of the model, it was applied to a case study examining the railroad network in the southeastern U.S. The public use version of the Interstate Commerce Commission\u27s Commodity Waybill Sample (CWS) provided flow data. The dissertation describes the procedure used to develop the case study and presents some results. The case points to major deficiencies in the CWS data which resulted in substantially less traffic in the network than is actually present. In general, given this limitation, the model behaved well and results appear reasonable, although not necessarily reflective of actual network conditions

Portable High-Performance Indexing for Vector Product Format Spatial Databases

Geo-spatial databases have an overall performance problem because of their complexity and large size. For this reason, many researchers seek new ways to improve the overall performance of geo-spatial databases. Typically, these research efforts are focused on complex indexing structures and query processing methods to capture the relationships between the individual features of fully-functional geo-spatial databases. Visualization applications, such as combat simulators and mission planning tools, suffer from the general performance problems associated with geo-spatial databases. This research focuses on building a high-performance geo-spatial database for visualization applications. The main approach is to simplify the complex data model and to index it with high-performance indexing structures. Complex features are reduced to simple primitives, then indexed using a combination of a disk-based array and B(+)-Trees. Test results show that there is a significant performance improvement gained by the new data model and indexing schema for low to medium zoom levels. For high zoom levels, there is a performance drop due to the indexing schema\u27s overhead

Advanced model-based risk reasoning on automatic railway level crossings

Safety is a core issue in the railway operation. In particular, as witnessed by accident/incident statistics, railway level crossing (LX) safety is one of the most critical points in railways. In the present paper, a Bayesian network (BN) based framework for causal reasoning related to risk analysis is proposed. It consists of a set of integrated stages, namely risk scenario definition, real field data collection and processing, BN model establishment and model performance validation. In particular, causal structural constraints are introduced to the framework forthe purpose of combining empirical knowledge with automatic learning approaches, thus to identify effective causalities and avoid inappropriate structural connections. Then, the proposed framework is applied to risk analysis of LX accidents in France. In details, the BN risk model is established on the basis of real field data and the model performance is validated. Moreover, forward and reverse inferences based on the BN risk model are performed to predict LX accident occurrence and quantify the contribution degree of various impacting factors respectively, so as to identify the riskiest factors. Besides, influence strength and sensitivity analyses are further carried out to scrutinize the influence strength of various causal factors on the LX accident occurrence likelihood and determine which factors the LX accident occurrence is most sensitive to. The main outputs of our study attest that the proposed framework is sound and effective in terms of risk reasoning analysis and offers significant insights on exploring practical recommendations to prevent LX accidents

North Carolina's first railroads, a study in historical geography

Part One of this study is a historical narrative that addresses the political, economic, and technological factors associated with the building of the first railroads in North Carolina, and their relationship to the railroad in Virginia. Both the Raleigh & Gaston Rail Road and the Wilmington & Raleigh Rail Road were completed in 1840. The latter did not run to Raleigh, as was the original intention when it was incorporated in 1833, but rather it terminated near the Weldon Toll Bridge on the Roanoke River where it connected to the Portsmouth & Roanoke Rail Road. The Raleigh & Gaston Rail Road, incorporated in 1835, connected to the Greensville & Roanoke Rail Road, a branch line of the Petersburg Rail Road, by its own bridge over the Roanoke approximately twelve miles west of Weldon. The two North Carolina railroads lacked the benefit of a connection that would bring them into a network. This part of the study concludes with the assessment that trade competition between the commercial centers of Norfolk and Petersburg adversely influenced early railroad development in North Carolina. Part Two of this study presents two spatial hypotheses. The first advances the position that early railroad development in North Carolina would mirror railroad development in southern Virginia to form an alignment of commercial centers north-to-south rather than east-to-west within physiographic regions. The second hypothesis suggests that the early railroads in North Carolina could have intersected north-to-south and east-to-west to form a productive network across physiographic regions. Of the many railroads proposed in North Carolina during the 1830s, the Waynesborough and Raleigh route seems the most likely component of an alternative network that would support the second hypothesis, if its practicality can be demonstrated by a plausible model. The empirical model prepared for this study replicates the conventions of a period railroad survey utilizing modern geographic tools and resources. The analysis of the resulting estimate supports the proposition that this railroad could have been built at that time had the interests in Raleigh and Wilmington agreed to one railroad to the Roanoke. The viability of other options suggests the possible that the rail network in North Carolina could have evolved differently under the same conditions

Interlacing Self-Localization, Moving Object Tracking and Mapping for 3D Range Sensors

This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects

포토리소그래피 검사 시스템의 이미지 분할을 위한 새로운 깊은 아키텍처

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021.8. 홍성수.In semiconductor manufacturing, defect detection is critical to maintain high yield. Typically, the defects of semiconductor wafer may be generated from the manufacturing process. Most computer vision systems used in semiconductor photolithography process inspection still have adopt to image processing algorithm, which often occur inspection faults due to sensitivity to external environment changes. Therefore, we intend to tackle this problem by means of converging the advantages of image processing algorithm and deep learning. In this dissertation, we propose Image Segmentation Detector (ISD) to extract the enhanced feature-maps under the situations where training dataset is limited in the specific industry domain, such as semiconductor photolithography inspection. ISD is used as a novel backbone network of state-of-the-art Mask R-CNN framework for image segmentation. ISD consists of four dense blocks and four transition layers. Especially, each dense block in ISD has the shortcut connection and the concatenation of the feature-maps produced in layer with dynamic growth rate for more compactness. ISD is trained from scratch without using recently approached transfer learning method. Additionally, ISD is trained with image dataset pre-processed by means of our designed image filter to extract the better enhanced feature map of Convolutional Neural Network (CNN). In ISD, one of the key design principles is the compactness, plays a critical role for addressing real-time problem and for application on resource bounded devices. To empirically demonstrate the model, this dissertation uses the existing image obtained from the computer vision system embedded in the currently operating semiconductor manufacturing equipment. ISD achieves consistently better results than state-of-the-art methods at the standard mean average precision which is the most common metric used to measure the accuracy of the instance detection. Significantly, our ISD outperforms baseline method DenseNet, while requiring only 1/4 parameters. We also observe that ISD can achieve comparable better results in performance than ResNet, with only much smaller 1/268 parameters, using no extra data or pre-trained models. Our experimental results show that ISD can be useful to many future image segmentation research efforts in diverse fields of semiconductor industry which is requiring real-time and good performance with only limited training dataset.반도체 제조에서 결함 검출은 높은 수율을 유지하는데 중요합니다. 전형적으로, 반도체 웨이퍼의 결함은 제조 공정에서 발생하고 있습니다. 반도체 포토리소그래피 공정 검사에 사용되는 대부분의 컴퓨터 비전 시스템들은 여전히 외부 환경 변화에 민감한 이미지 처리 알고리즘을 사용하고 있어서 검사 오류가 자주 발생하고 있습니다. 따라서, 이미지 처리 알고리즘의 장점과 딥 러닝의 장점을 융합하여 이 문제를 해결하려고 합니다. 이 논문에서 우리는 반도체 포토리소그래피 검사와 같이 훈련 데이터 세트가 제한된 상황에서 향상된 기능 맵을 추출하기 위해 이미지 분할 검출기(Image Segmentation Detector, 이하 ISD)를 제안합니다. ISD는 이미지 분할을 위한 최신 Mask R-CNN 프레임 워크의 새로운 백본 네트워크로 사용합니다. ISD는 4 개의 조밀한 블록과 4 개의 전환 레이어로 구성합니다. 특히, ISD의 각 조밀한 블록은 보다 컴팩트함을 위해 단축 연결 및 동적 성장률을 가지고 레이어에서 생성된 피쳐 맵을 결합하고 있습니다. ISD는 최근 적용하고 있는 전이 학습 방법을 사용하지 않고 처음부터 훈련합니다. 또한, ISD는 합성곱 신경망(Convolutional Neural Network, 이하 CNN)의 향상된 기능 맵을 추출하기 위해 우리가 설계한 이미지 필터를 통해 사전 처리된 이미지 데이터 세트로 훈련을 합니다. ISD의 설계 핵심 원칙 중 하나는 소형화로 실시간 문제를 해결하고 리소스에 제한이 있는 장치에 적용하는데 중요한 역할을 하게 합니다. 모델을 실증적으로 입증하기 위해 이 논문에서는 현재 운영 중인 반도체 제조 장비에 내장된 컴퓨터 비전 시스템에서 획득한 실제 이미지를 사용합니다. ISD는 가장 일반적인 성능 측정 지표인 평균 정밀도에서 최첨단 백본 네트워크 보다 일관되게 더 나은 성능을 얻습니다. 특히, ISD는 베이스 라인으로 삼은 DenseNet 보다 파라미터들이 4배 더 적지만, 성능이 우수 합니다. 우리는 또한 ISD가 Mask R-CNN 백본 네트워크로 주로 사용하는 ResNet 보다 268배 훨씬 더 적은 파라미터들을 가지고, 추가 데이터 또는 사전 훈련된 모델을 사용하지 않고, 성능에서 비슷하거나 더 나은 결과를 얻을 수 있음을 관찰합니다. 우리의 실험 결과들은 ISD가 제한된 훈련 데이터 세트만으로 실시간 및 우수한 성능을 요구하는 반도체 산업의 다양한 분야들에서 많은 미래의 이미지 분할 연구 노력에 유용할 수 있음을 보여줍니다.Chapter 1. Introduction １ 1.1. Background and Motivation ４ Chapter 2. Related Work １２ 2.1. Inspection Method １２ 2.2. Instance Segmentation １６ 2.3. Backbone Structure ２４ 2.4. Enhanced Feature Map ３５ 2.5. Detection Performance Evaluation ４７ 2.6. Learning Network Model from Scratch ５０ Chapter 3. Proposed Method ５２ 3.1. ISD Architecture ５２ 3.2. Pre-processing ６３ 3.3. Model Training ７１ 3.4. Training Objective ７３ 3.5. Setting and Configurations ７５ Chapter 4. Experimental Evaluation ７８ 4.1. Classification Results on ISD ８１ 4.2. Comparison with Pre-processing ８５ 4.3. Image Segmentation Results on ISD ９４ 4.3.1. Results on Suck-back State ９４ 4.3.2. Results on Dispensing State １０４ 4.4. Comparison with State-of-the-art Methods １１３ Chapter 5. Conclusion １２１ Bibliography １２７ 초록 １４６박

Observations and Laboratory Simulations of Tornadoes in Complex Topographical Regions

Aerial photos taken along the damage paths of the Joplin, MO, and Tuscaloosa-Birmingham, AL, tornadoes of 2011 captured and preserved several unique patterns of damage. In particular, a few distinct tree-fall patterns were noted along the Tuscaloosa-Birmingham tornado track that appeared highly influenced by the underlying topography. One such region was the focus of a damage survey and motivated laboratory vortex simulations with a 3-D foam representation of the underlying topography, in addition to simulations performed with idealized 2D topographic features, using Iowa State University\u27s tornado simulator. The purpose of this dissertation is to explore various aspects related to the interaction of a tornado or a tornado-like vortex with its underlying topography. Three topics are examined: 1) Analysis of tornado-induced tree-fall using aerial photography from the Joplin, MO, and Tuscaloosa-Birmingham, AL, tornadoes of 2011, 2) Laboratory investigation of topographical influences on a simulated tornado-like vortex, and 3) On the use of non-standard EF-scale damage indicators to categorize tornadoes