Extracting tennis statistics from wireless sensing environments

Creating statistics from sporting events is now widespread with most eorts to automate this process using various sensor devices. The problem with many of these statistical applications is that they require proprietary applications to process the sensed data and there is rarely an option to express a wide range of query types. Instead, applications tend to contain built-in queries with predened outputs. In the research presented in this paper, data from a wireless network is converted to a structured and highly interoperable format to facilitate user queries by expressing high level queries in a standard database language and automatically generating the results required by coaches

PENERAPAN OBJECT TRACKING DENGAN METODE ADAPTIVE PARTICLE FILTER UNTUK PELACAKAN BOLA PADA PERMAINAN

Data pergerakan bola dapat dimanfaatkan sebagai panduan untuk mengamati kejadian-kejadian pada pertandingan tenis yang telah berlangsung. Namun, untuk mendapatkan data pergerakan bola dari video pertandingan rentan terjadi kesalahan dalam pendeteksian objek, sehingga data yang dihasilkan terdapat noise. Berdasarkan alasan tesebut, penulis melakukan mining terhadap video pertandingan bola tenis dengan pendekatan object tracking, sehingga kesalahan deteksi ketika mendeteksi bola dapat dikurangi. Pendekatan tersebut diwujudkan dengan merancang model pelacakan bola dengan metode circle hough transform untuk mendeteksi lingkaran, kemudian dilanjutkan dengan metode pelacakan adaptive particle filter yang berfungsi untuk menghilangkan noise yang dihasilkan ketika melakukan deteksi lingkaran. Model tersebut dijalankan melalui proses-proses yang diantaranya adalah segmentasi citra, deteksi lingkaran, pelacakan objek dan diakhiri dengan koreksi lintasan. Model yang dirancang kemudian diimplementasikan pada bahasa pemrograman Phyton dan library OpenCV. Tahap terakhir dalam penelitian ini adalah melakukan eksperimen, eksperimen ini bertujuan untuk mendapatkan parameter masukan terbaik pada perangkat lunak, sehingga dapat diketahui efektifitas dari model yang telah diimplementasikan. Hasil eksperimen menunjukan bahwa video dengan jenis siaran pada lapangan hard court outdoor menghasilkan keluaran terbaik dengan rata-rata error sebesar 0,344, sedangkan hasil pengujian pada parameter lainnya harus disesuaikan dengan jenis video masukan agar mendapat error minimal.----------Ball movement data can be utilized as a guide for observing the events on the tennis matches that has lasted. However, the movement of the ball to get the data from the video game of the vulnerable object detection in error, so that the resulting data there is noise. Based on the reasons are, the author does mining against video game tennis ball with object tracking approach, so the error detection when it detects the ball can be reduced. The approach embodied by designing a model tracking ball with hough transform for circle method to detect circles, then proceed with adaptive particle filter tracking method that serves to eliminate noise generated when the detection loop. The model is run through processes such as image segmentation, object tracking, circle detection and end with correction trajectory. Model designed then implemented in the programming language Python and OpenCV library. The last stage in this research is doing experiments, this experiment aims to get the best input parameters in the software, so it can be known to the effectiveness of the model that has been implemented. Experimental results show that the type of video broadcast on an outdoor hard court field produce the best output with an average error of 0.344, whereas the test results on the other parameters must be adjusted to the type of video input so that it gets the error minimal

Real-time video vontent analysis tool for consumer media storage system

Current loop buffer organizations for very large instruction word processors are essentially centralized. As a consequence, they are energy inefficient and their scalability is limited. To alleviate this problem, we propose a clustered loop buffer organization, where the loop buffers are partitioned and functional units are logically grouped to form clusters, along with two schemes for buffer control, which regulate the activity in each cluster. Furthermore, we propose a design-time scheme to generate clusters by analyzing an application profile and grouping closely related functional units. The simulation results indicate that the energy consumed in the clustered loop buffers is, on average, 63 percent lower than the energy consumed in an uncompressed centralized loop buffer scheme, 35 percent lower than a centralized compressed loop buffer scheme, and 22 percent lower than a randomly clustered loop buffer scheme

A Novel and Effective Short Track Speed Skating Tracking System

This dissertation proposes a novel and effective system for tracking high-speed skaters. A novel registration method is employed to automatically discover key frames to build the panorama. Then, the homography between a frame and the real world rink can be generated accordingly. Aimed at several challenging tracking problems of short track skating, a novel multiple-objects tracking approach is proposed which includes: Gaussian mixture models (GMMs), evolving templates, constrained dynamical model, fuzzy model, multiple templates initialization, and evolution. The outputs of the system include spatialtemporal trajectories, velocity analysis, and 2D reconstruction animations. The tracking accuracy is about 10 cm (2 pixels). Such information is invaluable for sports experts. Experimental results demonstrate the effectiveness and robustness of the proposed system

Development of a Near-Field Magnetic Projectile Location System

Near-field magnetic principles and properties have been well studied and are used in a plethora of modern applications, ranging from medical applications to audio and video processing, and magnetic tracking. Current tracking applications are based in either AC or Pulsed-DC systems. Generally, AC systems have high resolution and accuracy, but perform very poorly in the presence of conducting magnetic materials. Pulsed-DC tracking has the benefit of not inducing large eddy currents in proximity to magnetic materials, thus increasing its overall accuracy. It has been suggested that pure DC systems are not feasible because they are unable to account for the presence of the Earth\u27s magnetic field.;It was the purpose of this research to propose and create a system, and develop an algorithm, that has the ability to determine the three-dimensional position and orientation of a permanent magnetic source; the position and orientation to be determined by information reported by a network of single-axis magnetic sensors. Methodology to account for the Earth\u27s magnetic field before, during, and after operation in order to remove ambient and environmental magnetic noise, much like a pulsed-DC system does, was also to be considered.;A center-finding algorithm was developed to determine position (x- and y-axis) based on the unique geometry of the B-field of the magnetic source at any point in three-dimensional space. Two degrees of orientation, elevation and rotation, were calculated from the position and the reported values of the magnetic sensors. The z-axis position was then determined given the analytical model and the other calculated values. In addition to the computed position, a six input Kalman tracker-estimator was developed and implemented using three dimensions of position and velocities to aid in predicting the path the magnetic source will take, based solely on kinematics, to reduce position-based sensor error.;The contribution of this research shows that is it not necessary to obtain three-axis magnetic data to track a magnetic source in three-dimensional space. When the distribution of the magnetic flux density is known, it is possible to determine three-dimensional position and orientation with only single-axis information.;Experimental testing verified the theoretical predictions of this statement. A rotational test apparatus was used to verify two-dimensional position and orientation, while a linear test apparatus verified position in three dimensions. The same magnetic source was used, while changing the orientation for each test. Initial findings allow the magnetic source to be tracked on the rotational testing apparatus to within a radial error of 3.9% (mean) and less than 6.4% (worst case) for predictions. The linear apparatus is able to track the z-axis component of the source which can be determined within 0.19% (mean) and 0.24% (worst case), and mean three-dimensional position of the magnetic source within 1.4% error. These results suggest that the novel method presented in this document is credible method for magnetic detection and tracking

Novel algorithms for tracking small and fast objects in low quality images.

In conventional computer vision systems, high image quality and long target exposure requirements are required. In this thesis, two algorithms to overcome such limitations of current computer vision systems have been proposed. The Pixel Exclusion Double Difference Algorithm (PEDDA) algorithm is a novel object detection algorithm that is able to detect fast moving objects in noisy images and suppress interference from large, low speed moving objects. The State-based “Observation, Analysis and Prediction” Target Election and Tracking Algorithm (SOAPtet) algorithm uses a deterministic state machine to guide the SOAPtet algorithm predictions. A novel stochastic based approach is also implemented in this algorithm to elect the target of interest from its candidates that are usually triggered by noise. A real time experimental system is developed based on the two algorithms. The experiment results show that this system detects up to 92.3% of moving objects in noisy environment and the tracking accuracy is up to 97.42%

An effective trajectory-based algorithm for ball detection and tracking with application to the analysis of broadcast sports video

Ph.DDOCTOR OF PHILOSOPH