Multi-camera analysis of soccer sequences

The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset

A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video

In this paper we propose a novel audio-visual feature-based framework, for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded soccer video. Specifically, the evidence gathered by various feature detectors is combined by means of a learning algorithm (a support vector machine), which infers the occurrence of an event, based on a model generated during a training phase, utilizing a corpus of 25 hours of content. The system is evaluated using 25 hours of separate test content. Following an evaluation of results obtained, it is shown for this case, that both high precision and recall statistics are achievable

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

Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation 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 for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts 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

The kindest cut: Enhancing the user experience of mobile tv through adequate zooming

The growing market of Mobile TV requires automated adaptation of standard TV footage to small size displays. Especially extreme long shots (XLS) depicting distant objects can spoil the user experience, e.g. in soccer content. Automated zooming schemes can improve the visual experience if the resulting footage meets user expectations in terms of the visual detail and quality but does not omit valuable context information. Current zooming schemes are ignorant of beneficial zoom ranges for a given target size when applied to standard definition TV footage. In two experiments 84 participants were able to switch between original and zoom enhanced soccer footage at three sizes - from 320x240 (QVGA) down to 176x144 (QCIF). Eye tracking and subjective ratings showed that zoom factors between 1.14 and 1.33 were preferred for all sizes. Interviews revealed that a zoom factor of 1.6 was too high for QVGA content due to low perceived video quality, but beneficial for QCIF size. The optimal zoom depended on the target display size. We include a function to compute the optimal zoom for XLS depending on the target device size. It can be applied in automatic content adaptation schemes and should stimulate further research on the requirements of different shot types in video coding

Tracking a table tennis ball for umpiring purposes

This study investigates tracking a table-tennis ball rapidly from video captured using low-cost equipment for umpiring purposes. A number of highly efficient algorithms have been developed for this purpose. The proposed system was tested using sequences capture from real match scenes. The preliminary results of experiments show that accurate and rapid tracking can be achieved even under challenging conditions, including occlusion and colour merging. This work can contribute to the development of an automatic umpiring system and also has the potential to provide amateur users open access to a detection tool for fast-moving, small, round objects

Audiovisual processing for sports-video summarisation technology

In this thesis a novel audiovisual feature-based scheme is proposed for the automatic summarization of sports-video content The scope of operability of the scheme is designed to encompass the wide variety o f sports genres that come under the description ‘field-sports’. Given the assumption that, in terms of conveying the narrative of a field-sports-video, score-update events constitute the most significant moments, it is proposed that their detection should thus yield a favourable summarisation solution. To this end, a generic methodology is proposed for the automatic identification of score-update events in field-sports-video content. The scheme is based on the development of robust extractors for a set of critical features, which are shown to reliably indicate their locations. The evidence gathered by the feature extractors is combined and analysed using a Support Vector Machine (SVM), which performs the event detection process. An SVM is chosen on the basis that its underlying technology represents an implementation of the latest generation of machine learning algorithms, based on the recent advances in statistical learning. Effectively, an SVM offers a solution to optimising the classification performance of a decision hypothesis, inferred from a given set of training data. Via a learning phase that utilizes a 90-hour field-sports-video trainmg-corpus, the SVM infers a score-update event model by observing patterns in the extracted feature evidence. Using a similar but distinct 90-hour evaluation corpus, the effectiveness of this model is then tested genencally across multiple genres of fieldsports- video including soccer, rugby, field hockey, hurling, and Gaelic football. The results suggest that in terms o f the summarization task, both high event retrieval and content rejection statistics are achievable

Data Hiding in Digital Video

With the rapid development of digital multimedia technologies, an old method which is called steganography has been sought to be a solution for data hiding applications such as digital watermarking and covert communication. Steganography is the art of secret communication using a cover signal, e.g., video, audio, image etc., whereas the counter-technique, detecting the existence of such as a channel through a statistically trained classifier, is called steganalysis. The state-of-the art data hiding algorithms utilize features; such as Discrete Cosine Transform (DCT) coefficients, pixel values, motion vectors etc., of the cover signal to convey the message to the receiver side. The goal of embedding algorithm is to maximize the number of bits sent to the decoder side (embedding capacity) with maximum robustness against attacks while keeping the perceptual and statistical distortions (security) low. Data Hiding schemes are characterized by these three conflicting requirements: security against steganalysis, robustness against channel associated and/or intentional distortions, and the capacity in terms of the embedded payload. Depending upon the application it is the designer\u27s task to find an optimum solution amongst them. The goal of this thesis is to develop a novel data hiding scheme to establish a covert channel satisfying statistical and perceptual invisibility with moderate rate capacity and robustness to combat steganalysis based detection. The idea behind the proposed method is the alteration of Video Object (VO) trajectory coordinates to convey the message to the receiver side by perturbing the centroid coordinates of the VO. Firstly, the VO is selected by the user and tracked through the frames by using a simple region based search strategy and morphological operations. After the trajectory coordinates are obtained, the perturbation of the coordinates implemented through the usage of a non-linear embedding function, such as a polar quantizer where both the magnitude and phase of the motion is used. However, the perturbations made to the motion magnitude and phase were kept small to preserve the semantic meaning of the object motion trajectory. The proposed method is well suited to the video sequences in which VOs have smooth motion trajectories. Examples of these types could be found in sports videos in which the ball is the focus of attention and exhibits various motion types, e.g., rolling on the ground, flying in the air, being possessed by a player, etc. Different sports video sequences have been tested by using the proposed method. Through the experimental results, it is shown that the proposed method achieved the goal of both statistical and perceptual invisibility with moderate rate embedding capacity under AWGN channel with varying noise variances. This achievement is important as the first step for both active and passive steganalysis is the detection of the existence of covert channel. This work has multiple contributions in the field of data hiding. Firstly, it is the first example of a data hiding method in which the trajectory of a VO is used. Secondly, this work has contributed towards improving steganographic security by providing new features: the coordinate location and semantic meaning of the object