a state-of-the-art real-time object detection system, and SORT, an object tracking framework based on data association and state estimation techniques. For training and testing, we use a given subset of the NCAA Basketball Dataset. As part of the bonus, we trained a two-layer LSTM to do action recognition. 1 Introduction
IPL Ball Detection Datasets. Ball detection datasets comprises three diffrent publicly available datasets for soccer, basketball, and volleyball sports. These datasets consist of approximately 34000 frames. They are labeled manually, frame by frame, for the purpose of academic studies in ball detection by the members of Image Processing Laboratory (IPL) of Sharif University of Technology.
Deep Learning YOLO - Basketball Detection. Motivation: As both basketball and machine learning enthusiast, I really want to combine these two elements in a project. Therefore, I used the YOLO algorithm to detect the basketball in a video and track the ball as it moves around in the video frames, drawing its previous positions as it moves.
Dataset. As mentioned above, the SpaceJam Basketball Action Dataset was used to train the R(2+1)D CNN model for video/action classification of basketball actions. The Repo contains two datasets (clips->.mp4 files and joints -> .npy files) of basketball single-player actions. The size of the two final annotated datasets is about 32,560 examples.
Fig. 3: Example of basket players detection using openCV pedestrian detection with HOG. return a single detection (box) for all those players. C. Color-Based Detection and Classiﬁcation The color-based detector performs player’s detection within a HOG box, which is a region of the original image classiﬁed as a pedestrian by the HOG detector.
Simone Francia developed a basketball action recognition dataset as shown in the video below. I've contacted Simone to get more details how his dataset can be used. Goal (Score) Eventually we also want to have a model which can identify when a player makes a goal (in basketball this can be a free throw which is one point, two or three points).
This could be a solution to solve the ball detection on soccer, but it’s clearly not an easy task. If anyone has a dataset of soccer balls (real and/or synthetic) we are interested in collaboration to train this neural network or develop a custom solution write an email to us at email@example.com. CONCLUSION FROM THE RESULTS
This dataset was released under a noncommercial license. See the xView dataset rules for more information. NWPU VHR-10. Northwestern Polytechnical University Very High Resolution-10. Academic papers. Multi-class geospatial object detection and geographic image classification based on collection of part detectors (Paywall)
You can contribute with 50 cents to research.https://www.paypal.me/franciasimoneDataset:https://github.com/simonefrancia/Basketball_DatasetLinkedin:https://w...
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