David A. Sadlier

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.

Automatic TV advertisement detection from MPEG bitstream

Abstract The Centre for Digital Video Processing at Dublin City University conducts concentrated research and development in the area of digital video management. The current stage of development is demonstrated on our Web-based digital video system called Fischlar (Proceedings of the Content based Multimedia Information Access, RIAO 2000, Vol. 2, Paris, France, 12–14 April 2000, p. 1390), which provides for efficient recording, analysing, browsing and viewing of digitally captured television programmes. Advertisement breaks during or between television programmes are typically recognised by a series of ‘black’ video frames simultaneously accompanying a depression in audio volume which separate each advertisement from one another by recurrently occurring before and after each individual advertisement. It is the regular prevalence of these flags that enables automatic differentiation between what is programme and what is a commercial break. This paper reports on the progress made in the development of this idea into an advertisement detector system that automatically detects the commercial breaks from the bitstream of digitally captured television broadcasts.

MPEG audio bitstream processing towards the automatic generation of sports programme summaries

The frequency subband scale-factors are fundamental components of MPEG-1 audio encoded bitstreams. Examination of scale-factor weights is sufficient for the establishment of an audio amplitude profile of an audio track. If, for sports programme TV broadcasts, the audio amplitude is assumed to primarily reflect the noise level exhibited by the commentator (and/or attending spectators), then, this vocal reaction to the significance of unfolding events may be used as a basis for summarisation. i.e. by relying on the exhilaration, or otherwise, expressed by the commentator/spectators, individual clips of the programme (e.g. camera shots), may be ranked according to their relative significance. A summary may then be produced by amalgamating (chronologically) any number of these clips corresponding to selected audio peaks.

Event detection based on generic characteristics of field-sports

In this paper, we propose a generic 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 generic 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 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.

User-Feedback on a Feature-Rich Photo Organiser

As the proliferation of digital photography increases, the software used to manage our increasingly large collections of digital photos becomes ever more important. In this paper we present the findings of a study which investigates how people view and interact with a set of photo management features. Specifically, a group of users are set the task of managing their own photos using a system built to encompass a wide range of photo organization functionality. They are then quizzed about their experiences in terms of their feature preferences, the general usability of the system, and other suggestions/requirements. Given these opinions, a basic estimation is then formed on what they like/dislike about the various aspects of the system, towards obtaining a more learned understanding of how we may develop a photo organizer that is optimal in terms of user satisfaction.

Semantic Analysis of Field Sports Video using a Petri-Net of Audio-Visual Concepts

The most common approach to automatic summarization and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets, which can be used for both semantic description and event detection within sports videos. Low-level algorithms to detect PCs using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of PCs is formally defined to describe video content. We call this a perception concept network–Petri-Net (PCN–PN) model. Using PCN–PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN–PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework.

Cross-media semantic indexing in the soccer domain

In this paper we describe collaborative and integrative work in the K-Space Network of Excellence. A goal of the work presented consists of combining results of the analysis of soccer videos with the semantic analysis of textual complementary sources, in order to support the semantic annotation and indexing of soccer videos. We present briefly a former approach to text-based semantic annotation and indexing of soccer videos as done in the MUMIS project and show in comparison the advances achieved within the K-Space project. A SMIL-based demonstrator has been implemented, documenting the approach and the resources used in our work. We present then briefly the set up of this demonstrator.

Image-based vehicle indexing for a seaport transportation surveillance system

In this paper we describe and evaluate two methods underpinning a surveillance-based content management system, designed for monitoring and profiling freight-based vehicular traffic in a seaport environment. The ‘InSPeCT’ system captures video footage of passing vehicles, and uses tailored optical character recognition (OCR) to index the footage according to vehicle license-plates and freight codes. The system provides advanced search techniques for the efficient retrieval of records, where each captured vehicle is profiled according to captured imagery and its associated interpretations. Underpinning this system is a method for detecting the boundaries of individual transits within sustained traffic flow. Considering it desirable to extend the indexing functionality of the system beyond OCR, a colour-based vehicle indexing approach is proposed and evaluated. All evaluations take place in the context of a system deployed in a busy national seaport.