Shot classification in broadcast soccer video.

Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.Event understanding systems, responsible for automatically generating human relatable event descriptions from video sequences, is an open problem in computer vision research that has many applications in the sports domain, such as indexing and retrieval systems for sports video. Background modelling and shot classification of broadcast video are important steps in event understanding in video sequences. Shot classification seeks to identify shots, i.e. the labelling of continuous frame sequences captured by a single camera action such as long shot, close-up and audience shot, while background modelling seeks to classify pixels in an image as foreground/background. Many features used for shot classification are built upon the background model therefore background modelling is an essential part of shot classification. This dissertation reports on an investigation into techniques and procedures for background modelling and classification of shots in broadcast soccer videos. Broadcast video refers to video which would typically be viewed by a person at home on their television set and imposes constraints that are often not considered in many approaches to event detection. In this work we analyse the performances of two background modelling techniques appropriate for broadcast video, the colour distance model and Gaussian mixture model. The performance of the background models depends on correctly set parameters. Some techniques offer better updating schemes and thus adapt better to the changing conditions of a game, some are shown to be more robust to changes in broadcast technique and are therefore of greater value in shot classification. Our results show the colour distance model slightly outperformed the Gaussian mixture model with both techniques performing similar to those found in literature. Many features useful for shot classification are proposed in the literature. This dissertation identifies these features and presents a detailed analysis and comparison of various features appropriate for shot classification in broadcast soccer video. Once a feature set is established, a classifier is required to determine a shot class based on the extracted features. We establish the best use of the feature set and decision tree parameters that result in the best performance and then use a combined feature set to train a neural network to classify shots. The combined feature set in conjunction with the neural network classifier proved effective in classifying shots and in some situations outperformed those techniques found in literature

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

An experiment in audio classification from compressed data

In this paper we present an algorithm for automatic classification of sound into speech, instrumental sound/ music and silence. The method is based on thresholding of features derived from the modulation envelope of the frequency limited audio signal. Four characteristics are examined for discrimination: the occurrence and duration of energy peaks, rhythmic content and the level of harmonic content. The proposed algorithm allows classification directly on MPEG-1 audio bitstreams. The performance of the classifier was evaluated on TRECVID test data. The test results are above-average among all TREC participants. The approaches adopted by other research groups participating in TREC are also discussed

Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic

Indexing of fictional video content for event detection and summarisation

This paper presents an approach to movie video indexing that utilises audiovisual analysis to detect important and meaningful temporal video segments, that we term events. We consider three event classes, corresponding to dialogues, action sequences, and montages, where the latter also includes musical sequences. These three event classes are intuitive for a viewer to understand and recognise whilst accounting for over 90% of the content of most movies. To detect events we leverage traditional filmmaking principles and map these to a set of computable low-level audiovisual features. Finite state machines (FSMs) are used to detect when temporal sequences of specific features occur. A set of heuristics, again inspired by filmmaking conventions, are then applied to the output of multiple FSMs to detect the required events. A movie search system, named MovieBrowser, built upon this approach is also described. The overall approach is evaluated against a ground truth of over twenty-three hours of movie content drawn from various genres and consistently obtains high precision and recall for all event classes. A user experiment designed to evaluate the usefulness of an event-based structure for both searching and browsing movie archives is also described and the results indicate the usefulness of the proposed approach

Indexing of fictional video content for event detection and summarisation

This paper presents an approach to movie video indexing that utilises audiovisual analysis to detect important and meaningful temporal video segments, that we term events. We consider three event classes, corresponding to dialogues, action sequences, and montages, where the latter also includes musical sequences. These three event classes are intuitive for a viewer to understand and recognise whilst accounting for over 90% of the content of most movies. To detect events we leverage traditional filmmaking principles and map these to a set of computable low-level audiovisual features. Finite state machines (FSMs) are used to detect when temporal sequences of specific features occur. A set of heuristics, again inspired by filmmaking conventions, are then applied to the output of multiple FSMs to detect the required events. A movie search system, named MovieBrowser, built upon this approach is also described. The overall approach is evaluated against a ground truth of over twenty-three hours of movie content drawn from various genres and consistently obtains high precision and recall for all event classes. A user experiment designed to evaluate the usefulness of an event-based structure for both searching and browsing movie archives is also described and the results indicate the usefulness of the proposed approach

Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable