Analysis of shot boundary detection techniques on a large video test suite

This thesis investigates how content-based indexing and retrieval systems can be used to analyse digital video. We focus particularly on the challenge of applying colour-analysis methods to large amounts of heterogeneous television broadcast video. Content-based systems are those which attempt to automatically analyse image or video documents by identifying and indexing certain features present in the documents. These features may include colour and texture, shape, and spatial locations. Digital video has become hugely important through the widespread use of the Internet and the increasing number of digital content providers supplying the commercial and domestic markets. The challenge facing the indexing of digital video information in order to support browsing and retrieval by users, is to design systems that can accurately and automatically process large amounts of heterogeneous video. The basic segmentation of video material into shots and scenes is the basic operation in the analysis of video content. Although many published methods of detecting shot boundaries exist, it is d ifficult to compare and contrast the available techniques. This is due to several reasons. Firstly, full system implementation details are not always published and this can make recreation of the systems difficult. Secondly, most systems are evaluated on small, homogeneous sequences of video. These results give little indication how such systems would perform on a broader range of video content types, or indeed how differing content types can affect system performance. As part of an ongoing video indexing and browsing project, our research has focused on the application of different methods of video segmentation to a large and diverse digital video collection. A particular focus is to examine how different segmentation methods perform on different video content types. With this information, it is hoped to develop a system capable of accurately segmenting a wide range of broadcast video. Oilier areas addressed in this thesis include an investigation of evaluation methods for digital video indexing systems, and the use of adaptive thresholds for segmentation of video into shots and scenes

Automatic indexing of video content via the detection of semantic events

The number, and size, of digital video databases is continuously growing. Unfortunately, most, if not all, of the video content in these databases is stored without any sort of indexing or analysis and without any associated metadata. If any of the videos do have metadata, then it is usually the result of some manual annotation process rather than any automatic indexing. Thus, locating clips and browsing content is difficult, time consuming and generally inefficient. The task of automatically indexing movies is particularly difficult given their innovative creation process and the individual style of many film makers. However, there are a number of underlying film grammar conventions that are universally followed, from a Hollywood blockbuster to an underground movie with a limited budget. These conventions dictate many elements of film making such as camera placement and editing. By examining the use of these conventions it is possible to extract information about the events in a movie. This research aims to provide an approach that creates an indexed version of a movie to facilitate ease of browsing and efficient retrieval. In order to achieve this aim, all of the relevant events contained within a movie are detected and classified into a predefined index. The event detection process involves examining the underlying structure of a movie and utilising audiovisual analysis techniques, supported by machine learning algorithms, to extract information based on this structure. The result is an indexed movie that can be presented to users for browsing/retrieval of relevant events, as well as supporting user specified searching. Extensive evaluation of the indexing approach is carried out. This evaluation indicates efficient performance of the event detection and retrieval system, and also highlights the subjective nature of video content

Efficient duration modelling in the hierarchical hidden semi-Markov models and their applications

Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically.Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly.Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy

Automatic mashup generation of multiple-camera videos

The amount of user generated video content is growing enormously with the increase in availability and affordability of technologies for video capturing (e.g. camcorders, mobile-phones), storing (e.g. magnetic and optical devices, online storage services), and sharing (e.g. broadband internet, social networks). It has become a common sight at social occasions like parties, concerts, weddings, vacations that many people are shooting videos at approximately the same time. Such concurrent recordings provide multiple views of the same event. In professional video production, the use of multiple cameras is very common. In order to compose an interesting video to watch, audio and video segments from different recordings are mixed into a single video stream. However, in case of non-professional recordings, mixing different camera recordings is not common as the process is considered very time consuming and requires expertise to do. In this thesis, we research on how to automatically combine multiple-camera recordings in a single video stream, called as a mashup. Since non-professional recordings, in general, are characterized by low signal quality and lack of artistic appeal, our objective is to use mashups to enrich the viewing experience of such recordings. In order to define a target application and collect requirements for a mashup, we conducted a study by involving experts on video editing and general camera users by means of interviews and focus groups. Based on the study results, we decided to work on the domain of concert video. We listed the requirements for concert video mashups such as image quality, diversity, and synchronization. According to the requirements, we proposed a solution approach for mashup generation and introduced a formal model consisting of pre-processing, mashupcomposition and post-processing steps. This thesis describes the pre-processing and mashup-composition steps, which result in the automatic generation of a mashup satisfying a set of the elicited requirements. At the pre-processing step, we synchronized multiple-camera recordings to be represented in a common time-line. We proposed and developed synchronization methods based on detecting and matching audio and video features extracted from the recorded content. We developed three realizations of the approach using different features: still-camera flashes in video, audio-fingerprints and audio-onsets. The realizations are independent of the frame rate of the recordings, the number of cameras and provide the synchronization offset accuracy at frame level. Based on their performance in a common data-set, audio-fingerprint and audio-onset were found as the most suitable to apply in generating mashups of concert videos. In the mashup-composition step, we proposed an optimization based solution to compose a mashup from the synchronized recordings. The solution is based on maximizing an objective function containing a number of parameters, which represent the requirements that influence the mashup quality. The function is subjected to a number of constraints, which represent the requirements that must be fulfilled in a mashup. Different audio-visual feature extraction and analysis techniques were employed to measure the degree of fulfillment of the requirements represented in the objective function. We developed an algorithm, first-fit, to compose a mashup satisfying the constraints and maximizing the objective function. Finally, to validate our solution approach, we evaluated the mashups generated by the first-fit algorithm with the ones generated by two other methods. In the first method, naive, a mashup was generated by satisfying only the requirements given as constraints and in the second method, manual, a mashup was created by a professional. In the objective evaluation, first-fit mashups scored higher than both the manual and naive mashups. To assess the end-user satisfaction, we also conducted a user study where we measured user preferences on the mashups generated by the three methods on different aspects of mashup quality. In all the aspects, the naive mashup scored significantly low, while the manual and first-fit mashups scored similarly. We can conclude that the perceived quality of a mashup generated by the naive method is lower than first-fit and manual while the perceived quality of the mashups generated by first-fit and manual methods are similar

Accessing spoken interaction through dialogue processing [online]

Zusammenfassung Unser Leben, unsere Leistungen und unsere Umgebung, alles wird derzeit durch Schriftsprache dokumentiert. Die rasante Fortentwicklung der technischen Möglichkeiten Audio, Bilder und Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt werden um die schriftliche Dokumentation von menschlicher Kommunikation, zum Beispiel Meetings, zu unterstützen, zu ergänzen oder gar zu ersetzen. Diese neuen Technologien können uns in die Lage versetzen Information aufzunehmen, die anderweitig verloren gehen, die Kosten der Dokumentation zu senken und hochwertige Dokumente mit audiovisuellem Material anzureichern. Die Indizierung solcher Aufnahmen stellt die Kerntechnologie dar um dieses Potential auszuschöpfen. Diese Arbeit stellt effektive Alternativen zu schlüsselwortbasierten Indizes vor, die Suchraumeinschränkungen bewirken und teilweise mit einfachen Mitteln zu berechnen sind. Die Indizierung von Sprachdokumenten kann auf verschiedenen Ebenen erfolgen: Ein Dokument gehört stilistisch einer bestimmten Datenbasis an, welche durch sehr einfache Merkmale bei hoher Genauigkeit automatisch bestimmt werden kann. Durch diese Art von Klassifikation kann eine Reduktion des Suchraumes um einen Faktor der Größenordnung 4­10 erfolgen. Die Anwendung von thematischen Merkmalen zur Textklassifikation bei einer Nachrichtendatenbank resultiert in einer Reduktion um einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen sie in thematische Segmente unterteilt werden. Ein neuer probabilistischer Ansatz sowie neue Merkmale (Sprecherinitia­ tive und Stil) liefern vergleichbare oder bessere Resultate als traditionelle schlüsselwortbasierte Ansätze. Diese thematische Segmente können durch die vorherrschende Aktivität charakterisiert werden (erzählen, diskutieren, planen, ...), die durch ein neuronales Netz detektiert werden kann. Die Detektionsraten sind allerdings begrenzt da auch Menschen diese Aktivitäten nur ungenau bestimmen. Eine maximale Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten Daten theoretisch möglich. Eine thematische Klassifikation dieser Segmente wurde ebenfalls auf einer Datenbasis durchgeführt, die Detektionsraten für diesen Index sind jedoch gering. Auf der Ebene der einzelnen Äußerungen können Dialogakte wie Aussagen, Fragen, Rückmeldungen (aha, ach ja, echt?, ...) usw. mit einem diskriminativ trainierten Hidden Markov Model erkannt werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen wie Frage/Antwort­Spielen erweitert werden (Dialogspiele). Dialogakte und ­spiele können eingesetzt werden um Klassifikatoren für globale Sprechstile zu bauen. Ebenso könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz erinnern und versuchen, diese in einer grafischen Repräsentation wiederzufinden. In einer Studie mit sehr pessimistischen Annahmen konnten Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische Repräsentation von Aktivität bestimmt. Dialogakte könnte in diesem Szenario ebenso nützlich sein, die Benutzerstudie konnte aufgrund der geringen Datenmenge darüber keinen endgültigen Aufschluß geben. Die Studie konnte allerdings für detailierte Basismerkmale wie Formalität und Sprecheridentität keinen Effekt zeigen. Abstract Written language is one of our primary means for documenting our lives, achievements, and environment. Our capabilities to record, store and retrieve audio, still pictures, and video are undergoing a revolution and may support, supplement or even replace written documentation. This technology enables us to record information that would otherwise be lost, lower the cost of documentation and enhance high­quality documents with original audiovisual material. The indexing of the audio material is the key technology to realize those benefits. This work presents effective alternatives to keyword based indices which restrict the search space and may in part be calculated with very limited resources. Indexing speech documents can be done at a various levels: Stylistically a document belongs to a certain database which can be determined automatically with high accuracy using very simple features. The resulting factor in search space reduction is in the order of 4­10 while topic classification yielded a factor of 18 in a news domain. Since documents can be very long they need to be segmented into topical regions. A new probabilistic segmentation framework as well as new features (speaker initiative and style) prove to be very effective compared to traditional keyword based methods. At the topical segment level activities (storytelling, discussing, planning, ...) can be detected using a machine learning approach with limited accuracy; however even human annotators do not annotate them very reliably. A maximum search space reduction factor of 6 is theoretically possible on the databases used. A topical classification of these regions has been attempted on one database, the detection accuracy for that index, however, was very low. At the utterance level dialogue acts such as statements, questions, backchannels (aha, yeah, ...), etc. are being recognized using a novel discriminatively trained HMM procedure. The procedure can be extended to recognize short sequences such as question/answer pairs, so called dialogue games. Dialog acts and games are useful for building classifiers for speaking style. Similarily a user may remember a certain dialog act sequence and may search for it in a graphical representation. In a study with very pessimistic assumptions users are able to pick one out of four similar and equiprobable meetings correctly with an accuracy ~ 43% using graphical activity information. Dialogue acts may be useful in this situation as well but the sample size did not allow to draw final conclusions. However the user study fails to show any effect for detailed basic features such as formality or speaker identity

Automatic Lecture Recording

Lecture recording has become a very common tool to provide students with additional media for their examination preparations. While its effort has to stay reasonable, only a very basic way of recording is done in many cases. Therefore, watching the resulting videos can get very boring completely independent of how interesting the original topic or session was. This thesis proposes a new approach to lecture recordings by letting distributed computers emulate the work of a human camera team, which is the natural way of creating attractive recordings. This thesis is structured in six chapters, starting with the examination of the current situation, and taking its constraints into account. The first chapter concludes with a reflection on related work. Chapter two is about the design of our prototype system. It is deduced from a human camera team in the real world which gets transferred into the virtual world. Finally, a detailed overview about all parts necessary for our prototype and their planned functionality is given. In chapter three, the implementation of all parts and tasks and the incidents occurring during implementation are described in detail. Chapter four describes the technical experiences made with the different parts during development, testing and evaluation with a view to functionality, performance, and an proposal towards future work. The evaluation of the whole system with students is presented and discussed in the fifth chapter. Chapter six concludes this thesis by summing up the facts and gives an outlook on future work

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Affective and Implicit Tagging using Facial Expressions and Electroencephalography.

PhDRecent years have seen an explosion of user-generated, untagged multimedia data, generating a need for efficient search and retrieval of this data. The predominant method for content-based tagging is through manual annotation. Consequently, automatic tagging is currently the subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users' responses to the multimedia content are analysed in order to generate descriptive tags. We approach this problem through the modalities of facial expressions and EEG signals. We investigate tag validation and affective tagging using EEG signals. The former relies on the detection of event-related potentials triggered in response to the presentation of invalid tags alongside multimedia material. We demonstrate significant differences in users' EEG responses for valid versus invalid tags, and present results towards single-trial classification. For affective tagging, we propose methodologies to map EEG signals onto the valence-arousal space and perform both binary classification as well as regression into this space. We apply these methods in a real-time affective recommendation system. We also investigate the analysis of facial expressions for implicit tagging. This relies on a dynamic texture representation using non-rigid registration that we first evaluate on the problem of facial action unit recognition. We present results on well-known datasets (with both posed and spontaneous expressions) comparable to the state of the art in the field. Finally, we present a multi-modal approach that fuses both modalities for affective tagging. We perform classification in the valence-arousal space based on these modalities and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information