Hierarchical recognition of intentional human gestures for sports video annotation

We present a novel technique for the recognition of complex human gestures for video annotation using accelerometers and the hidden Markov model. Our extension to the standard hidden Markov model allows us to consider gestures at different levels of abstraction through a hierarchy of hidden states. Accelerometers in the form of wrist bands are attached to humans performing intentional gestures, such as umpires in sports. Video annotation is then performed by populating the video with time stamps indicating significant events, where a particular gesture occurs. The novelty of the technique lies in the development of a probabilistic hierarchical framework for complex gesture recognition and the use of accelerometers to extract gestures and significant events for video annotation.

Automatic labeling of sports video using umpire gesture recognition

We present results on an extension to our approach for automatic sports video annotation. Sports video is augmented with accelerometer data from wrist bands worn by umpires in the game. We solve the problem of automatic segmentation and robust gesture classification using a hierarchical hidden Markov model in conjunction with a filler model. The hierarchical model allows us to consider gestures at different levels of abstraction and the filler model allows us to handle extraneous umpire movements. Results are presented for labeling video for a game of Cricket.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.

An immunological approach to raising alarms in video surveillance

Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three types of detectors for detecting abnormal activity are developed using negative selection. Tracks gathered by people's movements in a room are used for experimentation and results have shown that the classifier is able to discriminate abnormal from normal activities in terms of both trajectory and time spent at a location.

On the automatic indexing of cricket using camera motion parameters

This paper describes an application of camera motion estimation to index cricket games. The shots are labeled with the type of shot: glance left, glance right, left drive, right drive, left cut, right pull and straight drive. The method has the advantages that it is fast and avoids complex image segmentation. The classification of the cricket shots is done using an incremental learning algorithm. We tested the method on over 600 shots and the results show that the system has a classification accuracy of 74%.

Video genre categorization using audio wavelet coefficients

In this paper, we investigate the use of a wavelet transform-based analysis of audio tracks accompanying videos for the problem of automatic program genre detection. We compare the classification performance based on wavelet-based audio features to that using conventional features derived from Fourier and time analysis for the task of discriminating TV programs such as news, commercials, music shows, concerts, motor racing games, and animated cartoons. Three different classifiers namely the Decision Trees, SVMs, and k-Nearest Neighbours are studied to analyse the reliability of the performance of our wavelet features based approach. Further, we investigate the issue of an appropriate duration of an audio clip to be analyzed for this automatic genre determination. Our experimental results show that features derived from the wavelet transform of the audio signal can very well separate the six video genres studied. It is also found that there is no significant difference in performance with varying audio clip durations across the classifiers.

Combining NL processing and video data to query American football

We explore the use of natural language understanding and image processing to index and query American Football tapes. We present a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain, and a recognition system which uses the model to recognise American Football plays.

Dynamic shot suggestion filtering for home video based on user performance

This paper presents novel additions to our existing amateur media creation framework. The framework provides at-capture guidance to enable the home movie maker to realize their aesthetic and narrative goals and automation of post-production editing. A common problem with the amateur filming context is its contingent nature, which often results in the failure to gain footage vital to the user's goals, even with at-capture software embedding. Accordingly, we have modelled minimizing the difference between target and captured footage at a given time during filming as a probability distribution divergence problem. We apply two policies of feedback to the user on their performance, passive communication via a suggestion desirability measure, and active filtering of undesirable suggestions. We demonstrate the framework using each policy with a simulation of various user and filming situations with promising results.

Using video tutorials as a carrot-and-stick approach to learning

Traditional teaching styles practiced at universities do not generally suit all students' learning styles. For a variety of reasons, students do not always engage in learning in the courses in which they are enrolled. New methods to create and deliver educational material are available, but these do not always improve learning outcomes. Acknowledging these truths and developing and delivering educational material that provides diverse ways for students to learn is a constant challenge. This study examines the use of video tutorials within a university environment in an attempt to provide a teaching model that is valuable to all students, and in particular to those students who are not engaging in learning. The results of a three-year study have demonstrated that the use of well-designed, assessment-focused, and readily available video tutorials have the potential to improve student satisfaction and grades by enabling and encouraging students to learn how they want, when they want, and at a pace that suits their needs.