Football Player Detection
- bronkscottema
- american-football-players Dataset
- 495 images
Overview
This project started over 3 years ago, where I wanted to make something that would draw out football plays automatically. Last year I hit a break through in my python development where I could track players individually. Roboflow has allowed me to track players by position groups.
Classes
Some of them are straight forward like Center, QB (quarterback), db (defensive back), lb (linebacker), but the rest are identified as skill. That means an offensive player like Runningback, Fullback, Tightend, H-back, Wide Reciever.
The project in action
I haven't made a video with myself using roboflow but I will shortly. You can see the project on my linkedin and how it's grown and will continue to grow.
My LinkedIn
Surfer Spotting
- Surfline
- surfers Dataset
- 3702 images
Overview
The Surfline Surfer Spotting dataset contains images with surfers floating on the coast. Each image contains one classification called "surfer" but may contain multiple surfers.
Example Footage
Using this Dataset
There are several deployment options available, including inferring via API, webcam, and curl command.
Here is a code snippet for to hit the hosted inference API you can use. Here are code snippets for more languages
const axios = require("axios");
const fs = require("fs");
const image = fs.readFileSync("YOUR_IMAGE.jpg", {
encoding: "base64"
});
axios({
method: "POST",
url: "https://detect.roboflow.com/surfer-spotting/2",
params: {
api_key: "YOUR_KEY"
},
data: image,
headers: {
"Content-Type": "application/x-www-form-urlencoded"
}
})
.then(function(response) {
console.log(response.data);
})
.catch(function(error) {
console.log(error.message);
});
Download Dataset
On the versions tab you can select the version you like, and choose to download in 26 annotation formats.
volleyball_dataset
- MIKHAIL KLYUKIN
- Dataset-of-volleyball-actions Dataset
- 1236 images
Classes
- blocking - for players who have their arms under the net while the ball is crossing the net
- passing - for players who make the first touch below the net
- spiking - for palyers who make the touch (second or third) on their side of the court after which the ball goes to the other side
- setting - for players who make the touch after which other player from their team spikes
- serving - the first touch in the game
- digging - action after opponent spike, blok, action to save the ball in not ordinary situation, when player should fall, fall and run, use not typical to play
VOT2015
- VOT2015 Challenge
- objects Dataset
- 21216 images
VOT2015 Dataset
The dataset comprises 60 short sequences showing various objects in challenging backgrounds. The sequences were chosen from a large pool of sequences including the ALOV dataset, OTB2 dataset, non-tracking datasets, Computer Vision Online, Professor Bob Fisher’s Image Database, Videezy, Center for Research in Computer Vision, University of Central Florida, USA, NYU Center for Genomics and Systems Biology, Data Wrangling, Open Access Directory and Learning and Recognition in Vision Group, INRIA, France. The VOT sequence selection protocol was applied to obtain a representative set of challenging sequences. The dataset is automatically downloaded by the evaluation kit when needed, there is no need to separately download the sequences for the challenge.
Annotations
The sequences were annotated by the VOT committee using rotated bounding boxes in order to provide highly accurate ground truth values for comparing results. The annotations are stored in a text file with the format:
frameN: X1, Y1, X2, Y2, X3, Y3, X4, Y4
where Xi and Yi are the coordinates of corner i of the bounding box in frame N, the N-th row in the text file.
The bounding box was be placed on target such that at most ~30% of pixels within the bounding box corresponded to the background pixels, while containing most of the target. For example, in annotating a person with extended arms, the bounding box was placed such that the arms were not included. Note that in some sequences parts of objects rather than entire objects have been annotated. A rotated bounding box was used to address non-axis alignment of the target. The annotation guidelines have been applied at the judgement of the annotators.
Some targets were partially occluded or were partially out of the image frame. In these cases the bounding box were “inferred” by the annotator to fully contain the object, including the occluded part. For example, if a person’s legs were occluded, the bounding box should also include the non-visible legs.
The annotations have been conducted by three groups of annotators. Each annotator group annotated one third of the dataset and these annotations have been cross-checked by two other groups. The final annotations were checked by the coordinator of the annotation process. The final bounding box annotations have been automatically rectified by replacing a rotated bounding box by an axis-aligned if the ratio of the shortest and longest bounding-box side exceeded 0.95.
Annotators:
Gustavo Fernandez (coordinator)
Jingjing Xiao
Georg Nebehay
Roman Pflugfelder
Koray Aytac
futbol players
- Ilyes Talbi
- players-referee Dataset
- 163 images
Background Information
This dataset was curated and annotated by Ilyes Talbi, Head of La revue IA, a French publication focused on stories of machine learning applications.
Main objetive is to identify if soccer (futbol) players, the referree and the soccer ball (futbol).
The original custom dataset (v1) is composed of 163 images.
- Class 0 = players
- Class 1 = referree
- Class 2 = soccer ball (or futbol)
The dataset is available under the Public License.
Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
Dataset Versions
Version 7 (v7) - 163 images (raw images)
- Preprocessing: Auto-Orient, Modify Classes: 3 remapped, 0 dropped
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Augmentations: No augmentations applied
- Training Metrics: This version of the dataset was not trained
Version 2 (v2) - 163 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
- Augmentations: No augmentations applied
- Training Metrics: This version of the dataset was not trained
Version 3 (v3) - 391 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 0 dropped
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Augmentations:
- Outputs per training example: 3
- Rotation: Between -25° and +25°
- Shear: ±15° Horizontal, ±15° Vertical
- Brightness: Between -25% and +25%
- Blur: Up to 0.75px
- Noise: Up to 1% of pixels
- Bounding Box: Blur: Up to 0.5px
- Training Metrics: 86.4%mAP, 51.8% precision, 90.4% recall
Version 4 (v4) - 391 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 0 dropped
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Augmentations:
- Outputs per training example: 3
- Rotation: Between -25° and +25°
- Shear: ±15° Horizontal, ±15° Vertical
- Brightness: Between -25% and +25%
- Blur: Up to 0.75px
- Noise: Up to 1% of pixels
- Bounding Box: Blur: Up to 0.5px
- Training Metrics: 84.6% mAP, 52.3% precision, 85.3% recall
Version 5 (v5) - 391 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 2 dropped
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Only Class 0, which was remapped to players was included in this version
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Augmentations:
- Outputs per training example: 3
- Rotation: Between -25° and +25°
- Shear: ±15° Horizontal, ±15° Vertical
- Brightness: Between -25% and +25%
- Blur: Up to 0.75px
- Noise: Up to 1% of pixels
- Bounding Box: Blur: Up to 0.5px
- Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
- 98.8%mAP, 76.3% precision, 99.2% recall
Version 6 (v6) - 391 images
- Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 2 dropped
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Only Class 0, which was remapped to players was included in this version
- Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
- Augmentations:
- Outputs per training example: 3
- Rotation: Between -25° and +25°
- Shear: ±15° Horizontal, ±15° Vertical
- Brightness: Between -25% and +25%
- Blur: Up to 0.75px
- Noise: Up to 1% of pixels
- Bounding Box: Blur: Up to 0.5px
- Training Metrics: Trained from Scratch (no transfer learning employed)
- 95.5%mAP, 67.8% precision, 95.5% recall
Ilyes Talbi - LinkedIn | La revue IA
FinalGolf
- New Workspace
- Golf-balls Dataset
- 349 images
Shuttlecock
- Mathieu Cartron
- Shuttlecock Dataset
- 8053 images
Badminton Shuttlecock Object Detection
cyclists_database
- Paweł Brzozowski
- cyclists Dataset
- 5129 images
Cyclist Detection
Overview
This model helps detect people riding bycicles, and from which direction the cyclist can be seen (front, back, side).
Use Cases
Both self driving and sports broadcasting are great use cases for this model, as it gives great information about how the camera is positioned relative to the rider(s).
golfBall
- anna gaming
- ball Dataset
- 17460 images
Golf Ball Object Detection
Usage
This model will perform best on images + videos that are taken on a golf course (similar to photo in thumbnail and dataset).
It's a great model for sports broadcasting and other apps to have automated ball tracking, scoring, lost ball finding and more!
Boxpunch Detector
- MarkMcQuade
- Punches Dataset
- 239 images
Boxpunch Detector
Onboarding project for Roboflow
This project captures punch types thrown during boxing training