Homography-Based Passive Vehicle Speed Measuring

An apparatus for passively measuring vehicle speed includes at least one video camera or acquiring images of a roadway upon which at least one moving vehicle travels upon, each of the images comprising a plurality of pixels. A computer processes pixel data associated with the plurality of pixels, including using a adaptive background subtraction model to perform background subtraction on the pixel data to identify a plurality of foreground pixels, extracting a plurality of blobs from the foreground pixels, and rectifying the blobs to form a plurality of rectified blobs using a homography matrix. The homography matrix is obtained by comparing at least one known distance in the roadway with distances between the pixels. Using a planar homography transform, the moving vehicle is identified from the plurality of rectified blobs, wherein the respective ones of the plurality of rectified blobs include vehicle data associated with the moving vehicle.The speed of the moving vehicle is computed from the vehicle data

fine classification of complex motion pattern in fencing

Abstract The subject of this study was fencing and the object was to classify the fundamental motions of fencers by creating a library of movements. Based on this library, thus, the recognition of motions during a real fencing match can be made. Kinematic data were acquired by a motion capture system (Vicon). The automated algorithm that recognized motions is based on three steps: a Principal Component Analysis for data dimension reduction, an innovative wavelet-based analysis of signals and a feature extraction method. The algorithm was tested on high level fencing athletes and it was found to be robust with a 12% of misclassification rate. It gave a description of how atheletes move and showed that in real match athletes do not execute fundamental motions but they mix different techniques in order to surprise the opponent

BIOMECHANICAL DIFFERENCES IN LIFTERS WITH PRE-EXISTING INJURIES DURING THE SNATCH EXERCISE

Introduction: High intensity workouts are growing rapidly throughout the fitness industry. CrossFit and free weight workouts are becoming a popular standard throughout the exercise community. One of these exercises is the snatch. Weightlifting has been viewed as a high-risk sport as the rate of injury among other exercises is exponentially higher. Research has suggested that multiple repetitions can result in a breakdown in the biomechanical technique used by the lifters. This breakdown in mechanics could result in an increased risk for injuries. The purpose of this study is to quantify the biomechanical differences between lifters with pre-existing injuries and those without. We hypothesized that both sets of participants will exhibit observable breakdowns in technique, but the lifters with pre-existing injuries will show an increased loss of mechanics in body segments where they were injured previously. Methods: For this study at least 20 participants will be recruited to participate in this study. The participants in this study are between the ages of 18 and 45 years old. Participants will complete a snatch workout during a one-time visit that will last approximately 45 minutes to 1 hour. Utilizing a twelve-camera (10 infrared, 2 digital) 3D motion capture system (Opus 300+ Cameras, Qualisys, Goteborg, Sweden), kinematic data will be taken at a sampling frequency of 200 Hz in the frontal, sagittal, and transverse planes. 53 total markers will be placed onto the subject along with 6 marker plates. Once the markers are placed one static trial will be performed while the participant is holding a T-pose. Once the static trial is completed, 8 markers will be removed before the workout is completed. The weight for the workout will be based on 60% of the participant's one rep max for the snatch. The workout that participants will be completing is called, “Isabel� which consists of 30 repetitions for time. A rep count will be provided to the participant in order to maintain speed throughout the workout. The kinematic and kinetic variables of interest are 3D body segment positions and joint angles, bar height, ground reaction forces, and bar location relative to the lifter. The first repetition for each participant will be used as a baseline, and differences from the baseline for each variable will be calculated

Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks:a Validation-Based Approach

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader