Visual tracking for sports applications

Visual tracking of the human body has attracted increasing attention due to the potential to perform high volume low cost analyses of motions in a wide range of applications, including sports training, rehabilitation and security. In this paper we present the development of a visual tracking module for a system aimed to be used as an autonomous instructional aid for amateur golfers. Postural information is captured visually and fused with information from a golf swing analyser mat and both visual and audio feedback given based on the golfer's mistakes. Results from the visual tracking module are presented

Autonomous Sports Training from Visual Cues

Computer driven biometric analysis of athlete's movements have proven themselves as effective sports training tools. Most current systems rely on the use of retro-reflective markers or magnetic sensors to capture the motion of the athlete, so the biometric analysis can be performed. Video based training tools have also proved to be valuable instructional aids, however most require significant human interaction for analysis to be performed. This paper outlines an ongoing project focussed on capturing posture without the use of any markers or sensors, while still capturing enough information for an automated analysis to be performed. The approach taken to solving this problem is presented, as well as the current state of development of a an instructional aid for golfers

Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

Continuous Time Particle Filtering for fMRI

We construct a biologically motivated stochastic differential model of the neu-ral and hemodynamic activity underlying the observed Blood Oxygen Level De-pendent (BOLD) signal in Functional Magnetic Resonance Imaging (fMRI). The model poses a difficult parameter estimation problem, both theoretically due to the nonlinearity and divergence of the differential system, and computationally due to its time and space complexity. We adapt a particle filter and smoother to the task, and discuss some of the practical approaches used to tackle the difficulties, includ-ing use of sparse matrices and parallelisation. Results demonstrate the tractability of the approach in its application to an effective connectivity study.

Predictive and probabilistic tracking to detect stopped vehicles

Abstrac

Fusión sensorial para una plataforma de seguimiento

Este paper presenta una estrategia de seguimiento de objetivos aéreos basada en fusión sensorial para una plataforma de seguimiento óptico. Se describe la incorporación de datos de un sistema de posicionamiento global (GPS) a la información provista por un sensor óptico montado en la plataforma. Se especifica el método de recepción y procesamiento de la información y se utiliza un estimador de filtros de Kalman para la fusión de datos. El método propuesto es implementado experimentalmente y los resultados obtenidos evidencian un seguimiento robusto del objetivo en tiempo real.Sociedad Argentina de Informática e Investigación Operativ

Fusión sensorial para una plataforma de seguimiento

Este paper presenta una estrategia de seguimiento de objetivos aéreos basada en fusión sensorial para una plataforma de seguimiento óptico. Se describe la incorporación de datos de un sistema de posicionamiento global (GPS) a la información provista por un sensor óptico montado en la plataforma. Se especifica el método de recepción y procesamiento de la información y se utiliza un estimador de filtros de Kalman para la fusión de datos. El método propuesto es implementado experimentalmente y los resultados obtenidos evidencian un seguimiento robusto del objetivo en tiempo real.Sociedad Argentina de Informática e Investigación Operativ

Fusión sensorial para una plataforma de seguimiento

Este paper presenta una estrategia de seguimiento de objetivos aéreos basada en fusión sensorial para una plataforma de seguimiento óptico. Se describe la incorporación de datos de un sistema de posicionamiento global (GPS) a la información provista por un sensor óptico montado en la plataforma. Se especifica el método de recepción y procesamiento de la información y se utiliza un estimador de filtros de Kalman para la fusión de datos. El método propuesto es implementado experimentalmente y los resultados obtenidos evidencian un seguimiento robusto del objetivo en tiempo real.Sociedad Argentina de Informática e Investigación Operativ