Large-scale analysis of soccer matches using spatiotemporal tracking data

Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (approx 400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.

Identifying team style in soccer using formations learned from spatiotemporal tracking data

To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

A comparison of the performance of a brand of broad-band and several brands of narrow-band bat detectors in two different habitat types.

The use of bat detectors to monitor bat activity is common. Although several papers have compared the performance of different brands, none have dealt with the effect of different habitats nor have they compared narrow- and broad-band detectors. In this study the performance of four brands of ultrasonic bat detector, including three narrowband and one broad-band model, were compared for their ability to detect a 40 kHz continuous sound of variable amplitude along 100 metre transects. Transects were laid out in two contrasting bat habitat types: grassland and forest. Results showed that the different brands of detector differed in their ability to detect the source in terms of maximum and minimum detectable distance of the source. The rate of sound degradation with distance as measured by each brand was also different. Significant differences were also found in the performance of different brands in open grassland versus deep forest. No significant differences were found within any brand of detector. Though not as sensitive as narrow-band detectors, broad-band models hold an advantage in their ability to identify species where several species are found sympatrically.

Modeling kinect sensor noise for improved 3D reconstruction and tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy. © 2012 IEEE.

4D GPR tracking of water infiltration in fractured high-porosity limestone

Three thousand liters of water were infiltrated from a 4 m diameter pond to track flow and transport inside fractured carbonates with 20-40 % porosity. Sixteen time-lapse 3D Ground Penetrating Radar (GPR) surveys with repetition intervals between 2 hrs and 5 days monitored the spreading of the water bulb in the subsurface. Based on local travel time shifts between repeated GPR survey pairs, localized changes of volumetric water content can be related to the processes of wetting, saturation and drainage. Deformation bands consisting of thin sub vertical sheets of crushed grains reduce the magnitude of water content changes but enhance flow in sheet parallel direction. This causes an earlier break through across a stratigraphic boundary compared to porous limestone without deformation bands. This experiment shows how time-lapse 3D GPR or 4D GPR can non-invasively track ongoing flow processes in rock-volumes of over 100 m3.

Inorganic-organic composite particle as a staged delivery platform for plasmid DNA-based biopharmaceuticals

Infectious diseases such as SARS, influenza and bird flu may spread exponentially throughout communities. In fact, most infectious diseases remain major health risks due to the lack of vaccine or the lack of facilities to deliver the vaccines. Conventional vaccinations are based on damaged pathogens, live attenuated viruses and viral vectors. If the damage was not complete, the vaccination itself may cause adverse effects. Therefore, researchers have been prompted to prepare viable replacements for the attenuated vaccines that would be more effective and safer to use. DNA vaccines are generally composed of a double stranded plasmid that includes a gene encoding the target antigen under the transcriptional directory and control of a promoter region which is active in cells. Plasmid DNA (pDNA) vaccines allow the foreign genes to be expressed transiently in cells, mimicking intracellular pathogenic infection and inducing both humoral and cellular immune responses. Currently, because of their highly evolved and specialized components, viral systems are the most effective means for DNA delivery, and they achieve high efficiencies (generally >90%), for both DNA delivery and expression. As yet, viral-mediated deliveries have several limitations, including toxicity, limited DNA carrying capacity, restricted target to specific cell types, production and packing problems, and high cost. Thus, nonviral systems, particularly a synthetic DNA delivery system, are highly desirable in both research and clinical applications.

Bio-inspired plume tracking algorithm for UAVS

There is an increased interest on the use of UAVs for environmental research and to track bush fire plumes, volcanic plumes or pollutant sources. The aim of this paper is to describe the theory and results of a bio-inspired plume tracking algorithm. A memory based and gradient based approach, were developed and compared. A method for generating sparse plumes was also developed. Results indicate the ability of the algorithms to track plumes in 2D and 3D.