Jonas Sköld

Bio: Jonas Sköld is an academic researcher. The author has contributed to research in topics: Trajectory & Stereo camera. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Estimating 3D-trajectories from Monocular Video Sequences

Abstract: Tracking a moving object and reconstructing its trajectory can be done with a stereo camera system, since the two cameras enable depth vision. However, such a system would not work if one of the cameras fails to detect the object. If that happens, it would be beneficial if the system could still use the functioning camera to make an approximate trajectory reconstruction.In this study, I have investigated how past observations from a stereo system can be used to recreate trajectories when video from only one of the cameras is available. Several approaches have been implemented and tested, with varying results. The best method was found to be a nearest neighbors-search optimized by a Kalman filter. On a test set with 10000 golf shots, the algorithm was able to create estimations which on average differed around 3.5 meters from the correct trajectory, with better results for trajec-tories originating close to the camera.

Gravity as a Reference for Estimating a Person’s Height From Video

Abstract: Estimating the metric height of a person from monocular imagery without additional assumptions is ill-posed. Existing solutions either require manual calibration of ground plane and camera geometry, special cameras, or reference objects of known size. We focus on motion cues and exploit gravity on earth as an omnipresent reference 'object' to translate acceleration, and subsequently height, measured in image-pixels to values in meters. We require videos of motion as input, where gravity is the only external force. This limitation is different to those of existing solutions that recover a person's height and, therefore, our method opens up new application fields. We show theoretically and empirically that a simple motion trajectory analysis suffices to translate from pixel measurements to the person's metric height, reaching a MAE of up to 3.9 cm on jumping motions, and that this works without camera and ground plane calibration.

3D Ball Localization From A Single Calibrated Image

Abstract: Ball 3D localization in team sports has various applications including automatic offside detection in soccer, or shot release localization in basketball. Today, this task is either resolved by using expensive multi-views setups, or by restricting the analysis to ballistic trajectories. In this work, we propose to address the task on a single image from a calibrated monocular camera by estimating ball diameter in pixels and use the knowledge of real ball diameter in meters. This approach is suitable for any game situation where the ball is (even partly) visible. To achieve this, we use a small neural network trained on image patches around candidates generated by a conventional ball detector. Besides predicting ball diameter, our network outputs the confidence of having a ball in the image patch. Validations on 3 basketball datasets reveals that our model gives remarkable predictions on ball 3D localization. In addition, through its confidence output, our model improves the detection rate by filtering the candidates produced by the detector. The contributions of this work are (i) the first model to address 3D ball localization on a single image, (ii) an effective method for ball 3D annotation from single calibrated images, (iii) a high quality 3D ball evaluation dataset annotated from a single viewpoint. In addition, the code to reproduce this research will be made freely available at https://github.com/gabriel-vanzandycke/deepsport

Predicting trajectories of golf balls using recurrent neural networks

Abstract: This thesis is concerned with the problem of predicting the remaining part of the trajectory of a golf ball as it travels through the air where only the three-dimensional position of the ball is ca ...

Pathway Discover of Moving Objects using Color Segmentation and Region property

Abstract: This paper presents the real time algorithm of moving objects tracking and detection using region property and color segmentation. The real time of moving objects pathway finder is a vitaldifficultyproblem in human computersinterface and video observation. The attitude of track and detect a moving objects using color characteristic and movements has introduced with new techniques for automation. The tracking of video is a method of discovery the travelthing over specific reserve by use a color camera to narrate target bodies in successive video borders. Respecting to frame rate, the relationship could be especially troublesome in case of speedy moving of objects.In interchange case, the issue grows of randomness is the time in case of the tracking objects varying the direction following eventually. For this cases, the video tracking design model are classicallyexploit the progress model willportrays process the image of the target when it CHANGE for characteristicimaginableobjects movements. A development of tracking algorithm to the real time affecting bodies with unlike frames of the videotape by use color characteristic and movement is investigated and produced in this work.

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Abstract: Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications.