Evaluating a dancer's performance using Kinect-based skeleton tracking

In this work, we describe a novel system that automatically evaluates dance performances against a gold-standard performance and provides visual feedback to the performer in a 3D virtual environment. The system acquires the motion of a performer via Kinect-based human skeleton tracking, making the approach viable for a large range of users, including home enthusiasts. Unlike traditional gaming scenarios, when the motion of a user must by kept in synch with a pre-recorded avatar that is displayed on screen, the technique described in this paper targets online interactive scenarios where dance choreographies can be set, altered, practiced and refined by users. In this work, we have addressed some areas of this application scenario. In particular, a set of appropriate signal processing and soft computing methodologies is proposed for temporally aligning dance movements from two different users and quantitatively evaluating one performance against another

Vision-based analysis of pedestrian traffic data

Reducing traffic congestion has become a major issue within urban environments. Traditional approaches, such as increasing road sizes, may prove impossible in certain scenarios, such as city centres, or ineffectual if current predictions of large growth in world traffic volumes hold true. An alternative approach lies with increasing the management efficiency of pre-existing infrastructure and public transport systems through the use of Intelligent Transportation Systems (ITS). In this paper, we focus on the requirement of obtaining robust pedestrian traffic flow data within these areas. We propose the use of a flexible and robust stereo-vision pedestrian detection and tracking approach as a basis for obtaining this information. Given this framework, we propose the use of a pedestrian indexing scheme and a suite of tools, which facilitates the declaration of user-defined pedestrian events or requests for specific statistical traffic flow data. The detection of the required events or the constant flow of statistical information can be incorporated into a variety of ITS solutions for applications in traffic management, public transport systems and urban planning

A multi-modal dance corpus for research into real-time interaction between humans in online virtual environments

We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology are locally available to them, can learn choerographies with teacher guidance in an online virtual ballet studio. As the data corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers perform a number of fixed choreographies, which are both graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. Although the data corpus is tailored specifically for an online dance class application scenario, the data is free to download and used for any research and development purposes

Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment

PhD Forum: Investigating the performance of a multi-modal approach to unusual event detection

In this paper, we investigate the parameters under- pinning our previously presented system for detecting unusual events in surveillance applications [1]. The system identifies anomalous events using an unsupervised data-driven approach. During a training period, typical activities within a surveilled environment are modeled using multi-modal sensor readings. Significant deviations from the established model of regular activity can then be flagged as anomalous at run-time. Using this approach, the system can be deployed and automatically adapt for use in any environment without any manual adjustment. Experiments carried out on two days of audio-visual data were performed and evaluated using a manually annotated ground- truth. We investigate sensor fusion and quantitatively evaluate the performance gains over single modality models. We also investigate different formulations of our cluster-based model of usual scenes as well as the impact of dynamic thresholding on identifying anomalous events. Experimental results are promis- ing, even when modeling is performed using very simple audio and visual features

Enhanced visualisation of dance performance from automatically synchronised multimodal recordings

The Huawei/3DLife Grand Challenge Dataset provides multimodal recordings of Salsa dancing, consisting of audiovisual streams along with depth maps and inertial measurements. In this paper, we propose a system for augmented reality-based evaluations of Salsa dancer performances. An essential step for such a system is the automatic temporal synchronisation of the multiple modalities captured from different sensors, for which we propose efficient solutions. Furthermore, we contribute modules for the automatic analysis of dance performances and present an original software application, specifically designed for the evaluation scenario considered, which enables an enhanced dance visualisation experience, through the augmentation of the original media with the results of our automatic analyses

The coverage of the Irish marriage referendum shows that sometimes media ‘balance’ is impossible

Ireland recently voted to not only legalise same-sex marriage, but to become the first country to do so in a referendum, and to have it enshrined in the constitution. The campaign wasn’t just notable for the optimism and positivity of the ‘Yes’ campaign, though, with court decisions to ensure a “balanced” debate let to often farcical portrayals of the referendum by the broadcasters, argues Philip O’Connor

A very political project: Charles Haughey, social partnership and the pursuit of an Irish economic miracle, 1969-92

“Social Partnership” was the corporatist socio-economic governance system that functioned in Ireland 1987-2012. It is studied primarly from a political-economic perspective, focusing on problems of classification, e.g. whether it conformed to Keynesian “policy concertation” or “competitive corporatism”, or represented “networked policy making”, “advocacy coalition” or a functionalist co-opting of dissent. Economists question its impact on Ireland’s economic transformation, and comparative analyses find many aspects of it rendering it an “outlier”. Most studies concur that if not quite sui generis, it awaits explanation in domestic political terms. Few have examined its political dynamics or interaction in party-political conflict. These are the aims of this thesis. The absense of such an analysis is partly due to a poverty of sources. This thesis gained unique access to archives of key decision-making partnership bodies, including the NESC, the “Central Review Committee” (in D/Taoiseach), and union and business bodies, and also interviewed 35 key players. The thesis disproves generally accepted theories of partnership as a product of 1980s crisis and “Europeanisation” and a susequent evolution towards dysfunctional “groupthink”. It identifies instead its roots in party/interest group politics, and the political tendency which from the 1950s pursued “corporatism” as a means to overcome structural underdevelopment and drive socio-economic take-off. It identifies the political dynamic of the key 1987 PNR Agreement, and the compromises accounting for the system’s unique features and subsequent evolution. It establishes the political-entrepreneurial nexus of Charles Haughey collaborating with particular business and union leaders, and their agendas to re-organise state and economy. It argues that virtually all aspects of partnership as it maintained to 2012, from economic restructuring and local development to social reform, were initiated in 1987-92, marking that period as the decisive institutional rupture. It also identifies the political factors re-shaping partnership, gradually weakening its economic focus and re-orientating it to welfare state goals. This weakening had removed it from a central role by the time the state again faced crisis in 200

Robust pedestrian detection and tracking in crowded scenes

In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

Human motion reconstruction using wearable accelerometers

We address the problem of capturing human motion in scenarios where the use of a traditional optical motion capture system is impractical. Such scenarios are relatively commonplace, such as in large spaces, outdoors or at competitive sporting events, where the limitations of such systems are apparent: the small physical area where motion capture can be done and the lack of robustness to lighting changes and occlusions. In this paper, we advocate the use of body-worn wearable wireless accelerometers for reconstructing human motion and to this end we outline a system that is more portable than traditional optical motion capture systems, whilst producing naturalistic motion. Additionally, if information on the person's root position is available, an extended version of our algorithm can use this information to correct positional drift