Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions

A computational framework for unsupervised analysis of everyday human activities

In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner. A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity. Exploiting such representations, we propose a computational framework to discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments.Ph.D.Committee Chair: Aaron Bobick; Committee Member: Charles Isbell; Committee Member: David Hogg; Committee Member: Irfan Essa; Committee Member: James Reh

The First Stars

We review recent theoretical results on the formation of the first stars in the universe, and emphasize related open questions. In particular, we discuss the initial conditions for Population III star formation, as given by variants of the cold dark matter cosmology. Numerical simulations have investigated the collapse and the fragmentation of metal-free gas, showing that the first stars were predominantly very massive. The exact determination of the stellar masses, and the precise form of the primordial initial mass function, is still hampered by our limited understanding of the accretion physics and the protostellar feedback effects. We address the importance of heavy elements in bringing about the transition from an early star formation mode dominated by massive stars, to the familiar mode dominated by low mass stars, at later times. We show how complementary observations, both at high redshifts and in our local cosmic neighborhood, can be utilized to probe the first epoch of star formation.Comment: 38 pages, 10 figures, draft version for 2004 Annual Reviews of Astronomy and Astrophysics, high-resolution version available at http://cfa-www.harvard.edu/~vbromm

Dark Energy: Observational Evidence and Theoretical Models

The book elucidates the current state of the dark energy problem and presents the results of the authors, who work in this area. It describes the observational evidence for the existence of dark energy, the methods and results of constraining of its parameters, modeling of dark energy by scalar fields, the space-times with extra spatial dimensions, especially Kaluza---Klein models, the braneworld models with a single extra dimension as well as the problems of positive definition of gravitational energy in General Relativity, energy conditions and consequences of their violation in the presence of dark energy. This monograph is intended for science professionals, educators and graduate students, specializing in general relativity, cosmology, field theory and particle physics.Comment: Book, 380 p., 88 figs., 7 tables; 1st volume of three-volume book "Dark energy and dark matter in the Universe", ed. V. Shulga, Kyiv, Academperiodyka, 2013; ISBN 978-966-360-239-4, ISBN 978-966-360-240-0 (vol. 1). arXiv admin note: text overlap with arXiv:0706.0033, arXiv:1104.3029 by other author

A semantic concept for the mapping of low-level analysis data to high-level scene descriptions

Zusammen mit dem wachsenden Bedarf an Sicherheit wird eine zunehmende Menge an Überwachungsinhalten geschaffen. Um eine schnelle und zuverlässige Suche in den Aufnahmen hunderter oder tausender in einer einzelnenEinrichtung installierten Überwachungssensoren zu ermöglichen, istdie Indizierung dieses Inhalts im Voraus unentbehrlich. Zu diesem Zweckermöglicht das Konzept des Smart Indexing & Retrieval (SIR) durch dieErzeugung von high-level Metadaten kosteneffiziente Suchen. Da es immerschwieriger wird, diese Daten manuell mit annehmbarem Zeit- und Kostenaufwandzu generieren, muss die Erzeugung dieser Metadaten auf Basis vonlow-level Analysedaten automatisch erfolgen.Während bisherige Ansätze stark domänenabhängig sind, wird in dieserArbeit ein generisches Konzept für die Abbildung der Ergebnisse von lowlevelAnalysedaten auf semantische Szenenbeschreibungen präsentiert. Diekonstituierenden Elemente dieses Ansatzes und die ihnen zugrunde liegendenBegriffe werden vorgestellt, und eine Einführung in ihre Anwendungwird gegeben. Der Hauptbeitrag des präsentierten Ansatzes sind dessen Allgemeingültigkeit und die frühe Stufe, auf der der Schritt von der low-levelauf die high-level Repräsentation vorgenommen wird. Dieses Schließen in derMetadatendomäne wird in kleinen Zeitfenstern durchgeführt, während dasSchließen auf komplexeren Szenen in der semantischen Domäne ausgeführtwird. Durch die Verwendung dieses Ansatzes ist sogar eine unbeaufsichtigteSelbstbewertung der Analyseergebnisse möglich

BEHAVIORAL COMPOSITION FOR HETEROGENEOUS SWARMS

Research into swarm robotics has produced a robust library of swarm behaviors that excel at defined tasks such as flocking and area search, many of which have potential for application to a wide range of military problems. However, to be successfully applied to an operational environment, swarms must be flexible enough to achieve a wide array of specific objectives and usable enough to be configured and employed by lay operators. This research explored the use of the Mission-based Architecture for Swarm Composability (MASC) to develop mission-specific tactics as compositions of more general, reusable plays for use with the Advanced Robotic Systems Engineering Laboratory (ARSENL) swarm system. Three tactics were developed to conduct autonomous search of a geographic area and investigation of generated contacts of interest. The tactics were tested in live-flight and virtual environment experiments and compared to a preexisting monolithic behavior implementation completing the same task. Measures of performance were defined and observed that verified the effectiveness of solutions and confirmed the advantages that composition provides with respect to reusability and rapid development of increasingly complex behaviors.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Building a reliable and secure management framework for software-defined networks

Title from PDF of title page viewed December 15, 2021Dissertation advisor: Sejun SongVitaIncludes bibliographical references (pages 101-109)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021The Software-Defined Networking (SDN) technologies promise to enhance the performance and cost of managing both wired and wireless network infrastructures, functions, controls, and services (i.e., Internet of Things). However, centralized management in softwarization architecture poses new security, reliability, and scalability challenges. Significantly, the current OpenFlow Discovery Protocol (OFDP) in SDN induces substantial issues due to its gossipy, centralized, periodic, and tardy protocol. Furthermore, the problems are aggravated in the wireless and mobile SDN due to the dynamic topology churns and the lack of link-layer discovery methods. In this work, we tackle both security and reliability management issues in SDN. Specifically, we design and build a novel multitemporal cross-stratum discovery proto- col framework, which efficiently orchestrates different reliability monitoring mechanisms over SDN networks and synchronizes the control messages among various applications. It facilitates multiple discovery frequency timers for each target over different stratum instead of using a uniform discovery timer for the entire network. It supports many common reliability monitoring factors for registered applications by analyzing offline and online network architecture information such as network topologies, traffic flows, virtualization architectures, and protocols. The framework consists of traffic-aware discovery (TaDPole), and centrality-aware protocol (CAMLE) facilities. We implemented the framework on Ryu controller. Extensive Mininet experimental results validate that the framework significantly improves discovery message efficiency and makes the control traffic less bursty than OFDP with a uniform timer. It also reduces the network status discovery delay without increasing the control overhead. We then evaluated the security issues in SDN and proposed an SDN-based Wormhole Analysis using the Neighbor Similarity (SWANS) approach as a novel wormhole countermeasure in a Software-defined MANET. As SWANS analyses the similarity of neighbor counts at a centralized SDN controller, it apprehends wormholes not only without requiring any particular location information but also without causing significant communication and coordination overhead. SWANS also countermeasures various false-positive and false-negative scenarios generated by the Link Layer Discovery Protocol (LLDP) vulnerability. We performed extensive studies via both analysis and simulations. Our simulation results show that SWANS can detect wormhole attacks efficiently with low false-positive and false-negative rates.Introduction -- Background -- Literature review -- Traffic-aware discovery protocol for software-defined wireless and mobile networks -- Centrality-aware multitemporal discovery protocol for software-defined networks -- SDN-based wormhole analysis using the neighbor similarity for a Mobile Ad hoc Network (MANET) -- Conclusions and future wor

Branching Boogaloo: Botanical Adventures in Multi-Mediated Morphologies

FormaLeaf is a software interface for exploring leaf morphology using parallel string rewriting grammars called L-systems. Scanned images of dicotyledonous angiosperm leaves removed from plants around Bard’s campus are displayed on the left and analyzed using the computer vision library OpenCV. Morphometrical information and terminological labels are reported in a side-panel. “Slider mode” allows the user to control the structural template and growth parameters of the generated L-system leaf displayed on the right. “Vision mode” shows the input and generated leaves as the computer ‘sees’ them. “Search mode” attempts to automatically produce a formally defined graphical representation of the input by evaluating the visual similarity of a generated pool of candidate leaves. The system seeks to derive a possible internal structural configuration for venation based purely off a visual analysis of external shape. The iterations of the generated L-system leaves when viewed in succession appear as a hypothetical development sequence. FormaLeaf was written in Processing