This is a long-context, anonymized, clean, multi-turn and single-turn conversational dataset based on discord data scraped from a large variety of severs, big and small. The raw data for this version contained 51,826,268 messages. 5103788 (regex) + 696161 (toxic)/51826268, or 0.11% of the messages were removed.
The model was trained with ISSIA-CNR dataset (cameras 1,2,3,4) and SoccerPlayerDetection dataset (set 1). To run the trained model use the following command: python run_detector --path datasets/issia/filmrole5.avi --weights models/model_20201019_1416_final.pth --out_video out_video.avi --device <cpu|cuda>
ISSIA-CNR Soccer dataset contains six synchronized, long shot views of the football pitch acquired by six Full-HD DALSA 25-2M30 cameras. Three cameras are designated for each side of the playing-field, recording at 25 fps. Videos are acquired during matches of the Italian ’serie A’. There’re 20,000 annotated frames in the dataset annotated with ball position and player bounding boxes. Fig. 2
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 ...
from the ISSIA-CNR Soccer Dataset. Soccer Player. Detection dataset is created from two professional. football matches. Each match was recorded by three. broadcast cameras at 30 FPS with 1280720 ...
Meanwhile, you can browse through the websites of CNR STIIMA, CNR IREA and CNR INM where all people and activities, starting from may 2018, have been moved into.
This allows boosting the detection accuracy as larger visual context around the object of interest is taken into account. The method achieves state-of-the-art results when tested on publicly available ISSIA-CNR Soccer Dataset.
Download the dataset at http://www.issia.cnr.it/wp/dataset-cnr-fig/ and place them in the dataset directory. Launch Soccer Tracker.sln. Set solution configuration and platform to Release and x64. Set project properties (see images below) if using a different version of OpenCV. Enable OpenMP support; Build the solution
These are results of our player tracking procedure that will be presented at the 2014 AASRI Conference on Sports Engineering and Computer Science (SECS 2014)...
This allows boosting the detection accuracy as larger visual context around the object of interest is taken into account. The method achieves state-of-the-art results when tested on publicly available ISSIA-CNR Soccer Dataset. read more. PDF Abstract