Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 136-138).

Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model--called BigBackground--is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.

Considering todays growing society and developing technologies which are co-influential between each other, there is a larger scope of security concerns, traffic congestion due to improper planning and hence a greater need of more intelligent video surveillance. In this thesis, we have worked on developing such intelligent video surveillance system which mainly focusses on cell area such as parking spaces. The system operates on outdoor environment with a stationary camera; the main objective of this system is detecting and tracking of moving objects mainly cars. Two detection algorithms were developed using optical flow as core strategy. In the first algorithm the flow vectors were classified based on their magnitude and orientation; the GOMAG algorithm. The second algorithm used K-means method on the flow vectors to achieve the classification for moving object detection; the SKMO algorithm. A comparison analysis was done between the proposed algorithms and well known detection algorithms of background modeling and Otsu’s segmentation of flow vectors. The both proposed algorithms performed significantly better than background modeling and Otsu’s segmentation of flow vectors algorithms. The SKMO algorithm showed better stability and processed time efficiency than the GOMAG algorithm.

During this research, the concepts of Systems Engineering were applied to embedded system design. The objective was to apply the Systems Engineering methodology to the design of a particular embedded system. A Video Surveillancesystem was chosen as the particular embedded system. Systems Engineering concepts provide the foundation for an optimized design process and for the coordination between system modules. The functionality of the Video Surveillance system was achieved through the partitioning of the overall system functionality into three separate modules. The three modules were Image Capture, Image Processing and Image Transmission. The methodology employed resulted in a system that was flexible and portable. The three modules were designed using their own set of specifications and with completely defined linking interfaces. Following a concrete set of specifications resulted in a system, which can be modified at any later stage without the necessity of changing the whole architecture. The Video Surveillance system fulfilled the overall system requirements as well as those imposed by the subsystems. The partitioning of functionality resulted in ease of implementation and better upgradeability. Design based on Systems Engineering concepts provides for ease of integration. In addition, for modules that follow the same protocol, the existence of well defined interfaces enables connectivity to a variety of external units.

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / The Law Enforcement Aerial Platform System (LEAPS), designed and integrated by Spiral Technology, Inc., was architected to marry airborne sensors and ground-based instrumentation in support of and to augment the Law Enforcement and/or Disaster Response and Recovery agencies of counties and municipalities. The mission of LEAPS is to provide an affordable reliable manned or unmanned aerial surveillance system that readily integrates with existing Law Enforcement's and Local Government's infrastructures. The initial sensors being integrated into the LEAPS concept include both Visible Spectrum and Infrared Imager. Salient requirements for LEAPS include: Ground Control of Airborne Sensors; Sensor Data captured and archived on the ground with time-tag and geographic location data; and Controlled Custody and Preservation of Sensor Data as Evidentiary Material This paper describes the LEAPS System Development Effort.

High capacity wireless and xed-line broadband services have a relatively small footprint over South Africa's vast expanse. This results in many rural areas, as well as military communication when deployed, relying on low-bandwidth communication networks instead, making live video communication over these links impractical. Traditional and advanced data compression methods cannot produce the payload reduction required for video use over these bandwidths. Instead, a model-based vision system is used to address this problem. This is not video compression but rather image understanding and representation in the context of prior models of the observed object. Markerless human tracking and pose recovery are the specific interests of this research. Markerless human pose tracking is a relatively new and growing field of image processing. It has many potential areas of application apart from low-bandwidth video communication, including the medical field, sporting arena, security and surveillance and human-machine interaction. As multimedia technologies continue to grow and improve, pose tracking systems have the potential to be used more and more. While a few markerless tracking devices are beginning to emerge, many currently available commercial motion capture systems require the use of a special suit and markers or sensors. This makes them very impractical for easy everyday, anywhere use. Current research in computer vision and image processing incorporates a significant focus on the development of markerless approaches to human motion capture. This dissertation looks at a complete markerless human pose tracking system which can be split into four distinct but interlinking stages: the image capture, image processing, body model and optimisation stages. After video data from multiple camera views is captured, the processing stage extracts image cues such as silhouettes, 2-D edges and 3-D colour volumetric reconstruction. Following the basic principle of a model-based approach, a 24 degree-of-freedom superellipsoid body model is fitted to the observed image cue data. An objective function is used to measure the closeness of this match. A number of different optimisation approaches are examined for use in refining and finding the best fitting body pose for each image frame. These approaches are all based around Stochastic Meta Descent (SMD) optimisation with SMD by itself, SMD in a hierarchical approach, SMD with pose prediction and Smart Particle Filtering, SMD inside a particle filter framework, all explored. The performance of the system with the various optimisation approaches is tested using the HumanEvaII datasets. These datasets contain a number of different subjects performing a variety of actions while wearing ordinary clothes. They contain markerbased ground-truth data obtained using a ViconPeak motion capture system. This allows a relative error measurement of the predicted poses to be calculated. With its robustness to clutter and occlusion, the Smart Particle Filter approach is shown to give the best results. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.

Typescript. Includes bibliographical references (p. 51-53).

Abstract An unparalleled surge in video surveillance has occurred in recent years, due to some tragic events such as terror attacks, bank robberies and the activities of organized crime. Video surveillance technology has advanced significantly, which has even enabled the automatic tracking of individuals. However, in the opinion of the public the increase in security has brought about a decrease in personal privacy. Through video surveillance citizens could be monitored more easily than ever before, thus considerably intruding into their personal privacy. It was assumed that security and privacy in video surveillance was a zero-sum game in which citizens were forced to choose one over the other. This study was based on the belief that this notion is false. It was assumed that it can be possible to keep personal privacy while guaranteeing the utmost security. A solution to this issue was sought using Hevner’s design science research guidelines and design science research cycles. A video surveillance system was designed and constructed that would protect the personal privacy of uninvolved individuals under surveillance while still providing a high level of security, namely the Privacy Enhancing Video Surveillance system PEVS. PEVS protected the privacy of individuals by automatically scrambling the image regions where people were present in video streams. If a criminal act should take place, it was possible, with the proper authorization, to selectively unscramble the data of individuals of interest to analyze the situation. This enabled to analyze the situation without intruding into the privacy of uninvolved people on the one hand, while on the other hand using the data as evidence of possible criminal activity. Hence, the privacy of individuals was protected while maintaining the same level of security. PEVS provided the first technology-based video surveillance solution, which showed only relevant individuals in the image while leaving the identity of everyone else unrevealed. Therefore, the main contribution of this thesis was the construction of a novel approach to video surveillance systems, capable of selectively protecting the privacy of individuals. This included introducing an architecture for a privacy preserving video surveillance system, which consisted of several sub-constructs. These included storage techniques for privacy data and shadow detection and segmentation methods, which increased the accuracy and speed of previous methods. Further, novel security and privacy metrics for video surveillance were introduced. The overall system was a significant improvement over the existing knowledge base that has thus far seen only first steps to selective privacy protection but has failed to provide a complete system. / Tiivistelmä Videovalvonnassa on tapahtunut viime vuosina merkittävää kasvua johtuen järkyttävistä tapahtumista kuten terrori-iskut, pankkiryöstöt ja järjestäytyneen rikollisuuden toimet. Videovalvontateknologia on kehittynyt merkittävästi mahdollistaen jopa yksittäisten ihmisten automaattisen seurannan. Turvallisuuden lisääntymisen katsotaan kuitenkin vähentäneen yksityisyyttä. Videovalvonnan avulla ihmisiä pystytään seuraamaan helpommin kuin koskaan aikaisemmin tunkeutuen täten heidän yksityisyytensä alueelle. On oletettu, että turvallisuus ja yksityisyys videovalvonnassa on nollasummapeliä, jossa kansalaisten on valittava yksityisyyden ja turvallisuuden välillä. Tämä tutkimus perustuu olettamukseen, että edellä esitetty ei pidä paikkaansa, vaan että on mahdollista suojata yksityisyys samalla taaten täysi turvallisuus. Ratkaisua tähän ongelmaan etsittiin suunnittelutieteellisen tutkimuksen avulla. Työssä suunniteltiin ja toteutettiin videovalvontajärjestelmä PEVS (Privacy Enhancing Video Surveillance system), joka suojaa valvonnanalaisten sivullisten yksityisyyttä ja siitä huolimatta tuottaa korkean turvallisuustason.. PEVS suojaa henkilöiden yksityisyyttä salaamalla automaattisesti videoaineistosta ne kuva-alat, joissa esiintyy ihmisiä. Mikäli laitonta toimintaa havaittaisiin, olisi riittävillä käyttöoikeuksilla mahdollista purkaa salaus mielenkiinnon kohteena olevien henkilöiden kohdalta tilanteen analysoimiseksi. Tämä mahdollisti yhtäältä puuttumattomuuden sivullisten yksityisyyteen ja toisaalta tiedon käyttämisen todistusaineistona mahdollisen rikoksen tutkimisessa. Tällä järjestelmällä yksityisyys oli mahdollista suojata samanaikaisesti, kun turvallisuudesta huolehdittiin. PEVS mahdollisti ensimmäistä kertaa maailmassa videovalvonnan, joka näyttää vain relevantit henkilöt jättäen muiden henkilöllisyyden paljastamatta. Sen takia tämän tutkimuksen merkittävin kontribuutio oli uudenlaisen lähestymistavan kehittäminen videovalvontaan, joka kykenee valikoivasti suojelemaan ihmisten yksityisyyttä. Tämä ratkaisu sisältää yksityisyyden suojaavan, useita rakenneosia sisältävän videovalvontajärjestelmäarkkitehtuurin esittelyn. Rakenneosiin kuuluu yksityisen tiedon tallennusmenetelmiä ja varjontunnistus- ja segmentointimetodeja, jotka paransivat aiemmin käytettyjen metodien tarkkuutta ja nopeutta. Lisäksi esiteltiin uudenlainen turvallisuus- ja yksityisyysmetriikka videovalvonnalle. Toteutettu järjestelmä on huomattava lisäys nykytietämykseen, jossa yksityisyyden suojan osalta on otettu vasta ensiaskelia ja joka ei mahdollista kattavaa järjestelmää.

Le travail présenté dans ce mémoire a pour objectif le développement d'une nouvelle architecture pour les systèmes de vidéosurveillance. Tout d'abord, une étude bibliographique nous a conduit à classer les systèmes existants selon le niveau de leurs applications qui dépend directement des fonctions analytiques exécutées. Nous avons également constaté que les systèmes habituels traitent toutes les données enregistrées alors que réellement une faible partie des scènes sont utiles pour l'analyse. Ainsi, nous avons étendu l'architecture ordinaire des systèmes de surveillance par une phase de pré-analyse qui extrait et simplifie les régions d'intérêt en conservant les caractéristiques importantes. Deux méthodes différentes pour la pré-analyse dans le contexte de la vidéosurveillance ont été proposées : une méthode de filtrage spatio-temporel et une technique de modélisation des objets en mouvement. Nous avons contribué, aussi, par l'introduction du concept de la scalabilité orientée vers l'application à travers une architecture multi-niveaux applicatifs pour les systèmes de surveillance. Les différents niveaux d'applications des systèmes de vidéosurveillance peuvent être atteints incrémentalement pour répondre aux besoins progressifs de l'utilisateur final. Un exemple de système de vidéosurveillance respectant cette architecture et utilisant nos méthodes de pré-analyse est proposé. / The work presented in this thesis aims to develop a new architecture for video surveillance systems. Firstly, a literature review has led to classify the existing systems based on their applications level which dependents directly on the performed analytical functions. We, also, noticed that the usual systems treat all captured data while, actually, a small part of the scenes are useful for analysis. Hence, we extended the common architecture of surveillance systems with a pre-analysis phase that extracts and simplifies the regions of interest with keeping the important characteristics. Two different methods for preanalysis were proposed : a spatio-temporal filtering and a modeling technique for moving objects. We contributed, too, by introducing the concept of application-oriented scalability through a multi-level application architecture for surveillance systems. The different applications levels can be reached incrementally to meet the progressive needs of the enduser. An example of video surveillance system respecting this architecture and using the preanalysis methods was proposed.

Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic.