text: A novel method to identify trampoline skills using a single video camera is proposed herein. Conventional computer vision techniques are used for identification, estimation, and tracking of the gymnast's body in a video recording of the routine. For each frame, an open source convolutional neural network is used to estimate the pose of the athlete's body. Body orientation and joint angle estimates are extracted from these pose estimates. The trajectories of these angle estimates over time are compared with those of labelled reference skills. A nearest neighbour classifier utilising a mean squared error distance metric is used to identify the skill performed. A dataset containing 714 skill examples with 20 distinct skills performed by adult male and female gymnasts was recorded and used for evaluation of the system. The system was found to achieve a skill identification accuracy of 80.7 for the dataset.
  @cite_5: Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions.
  @cite_9: Abstract We describe a conceptually simple algorithm for assigning judgement scores to rhythmic gymnastic movements, which could improve scoring objectivity and reduce judgemental bias during competitions. Our method, implemented as a real-time computer vision software, takes a video shot or a live performance video stream as input and extracts detailed velocity field information from body movements, transforming them into specialized spatio-temporal image templates. The collection of such images over time, when projected into a velocity covariance eigenspace, trace out unique but similar trajectories for a particular gymnastic movement type. By comparing separate executions of the same atomic gymnastic routine, our method assigns a quality judgement score that is related to the distance between the respective spatio-temporal trajectories. For several standard gymnastic movements, the method accurately assigns scores that are comparable to those assigned by expert judges. We also describe our rhythmic gymnastic video shot database, which we have made freely available to the human movement research community. The database can be obtained at http: www.milegroup.net apps gymdb .
  @cite_11: 
summary: In @cite_11 , inertial sensors were used to measure body point acceleration and orientation. The gymnast was required to wear a body suit containing ten inertial measurement units. The sensor data streams were transformed into a feature sequence for classification. For each skill, a motion template was learned. The feature sequence of the unknown trampoline motions were compared with a set of skill templates using a variant of dynamic time warping. The best accuracy achieved was 84.7 A survey of vision-based methods for general human motion representation, segmentation and recognition can be found in @cite_5 . In @cite_9 , judging of rhythmic gymnastics skills from video was investigated. The movement of the gymnast was tracked using optical flow. Velocity field information was extracted across all frames of a skill and projected into a velocity covariance eigenspace. Similar movements were found to trace out unique, but similar, trajectories. New video recordings were classified based on their distance from reference trajectories. The system's specificity was approximately 85