Project created and maintained by Mohamed Traore, creator of Roboflow Universe computer vision projects and models like: 1. Face Detection Computer Vision Project - Over 110k views and 6k downloads 2. rock-paper-scissors Computer Vision Project - Over 150k views and 8k downloads 3. Construction Site Safety Computer Vision Project - Over 115k views and 5k downloads 4. Basketball Players Computer Vision Project - Over 20k views and 1k downloads 5. Retail Coolers Computer Vision Project - Over 10k views and 400 downloads

• Roboflow Forum profile

This project contains some annotated images forked/cloned from the following Public Domain projects on Roboflow Universe: Basketball Players Computer Vision Project

NBA Players Detection

## Overview

NBA Players Detection is a dataset for object detection tasks - it contains Basketball Players annotations for 71 images.

## 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.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Here are a few use cases for this project:

1. Sports Analytics and Player Performance Tracking: Use the Basketball computer vision model to automatically identify and track players, their gear, and ball movement during live games or recorded footage. Analyze player movements, shot accuracy, and on-court strategies for coaching and performance improvement purposes.

2. Sports Equipment and Apparel Marketing: Utilize the Basketball model to recognize and analyze the presence of specific brands of shoes, shorts, shirts, and other gear worn by players during games. This data can help determine the impactfulness of marketing campaigns and partnerships, as well as track market share and trends in the basketball apparel industry.

3. Automated Video Highlights and Summaries: Employ the Basketball computer vision model to analyze basketball game footage and automatically detect key moments, such as successful shots, impressive dribbling, and notable plays. These highlights can be compiled into summary videos for fans, news outlets, or social media platforms.

4. Injury Prevention and Rehabilitation: Apply the Basketball model to study player movements, body positions, and equipment during games to help recognize patterns that lead to injuries. Insights derived from this analysis can guide changes in training, game strategies, or equipment design to reduce the risk of injuries and assist in effective rehabilitation.

5. Enhanced Fan Experience and Engagement: Utilize the Basketball computer vision model in sports bars, stadiums, or at-home viewing setups to create interactive experiences for fans. Identify relevant objects within the game and display information, statistics, or trivia based on what's happening in real-time. This encourages an immersive environment, keeping fans engaged and entertained.

Here are a few use cases for this project:

1. Automatic Game Analysis: Analyze basketball matches in real-time or post-game to provide insights such as player movements, ball possession, and scoring opportunities. This could benefit sports analysts, coaches, and teams in improving their strategies and understanding of individual players' performance.

2. Player Performance Tracking: Monitor and evaluate individual player performance during practice sessions or games using identified player and ball classes. This could help in personalized training, skill development, and detecting strengths and weaknesses of each player.

3. Crowd Management and Security: Enhance stadium security and manage crowds during basketball events. The model can be used to detect unauthorized persons entering the court, monitor player and crowd interactions, and ensure overall safety during games.

4. Interactive Basketball Applications: Develop interactive apps or games that use augmented reality (AR) or virtual reality (VR) to simulate real-life basketball playing experiences. The computer vision model could help track ball movement and player positions for a more immersive and realistic gaming experience.

5. Marketing and Advertising: Analyze audience engagement during basketball games for targeted marketing and advertising campaigns. By detecting the presence of specific players, the model could help identify the most popular players and recommend athlete endorsements or product placements to relevant brands.

Project created and maintained by Mohamed Traore, creator of Roboflow Universe computer vision projects and models like: 1. Face Detection Computer Vision Project - Over 110k views and 6k downloads 2. rock-paper-scissors Computer Vision Project - Over 150k views and 8k downloads 3. Construction Site Safety Computer Vision Project - Over 115k views and 5k downloads 4. Basketball Players Computer Vision Project - Over 20k views and 1k downloads 5. Retail Coolers Computer Vision Project - Over 10k views and 400 downloads

• Roboflow Forum profile

This project contains some annotated images forked/cloned from the following Public Domain projects on Roboflow Universe:

• Parking Lot Occupancy Detection Computer Vision Project
• UtmachJust_Dataset Computer Vision Project
• PARKING LOT OCCUPANCY DETECTION Computer Vision Project
• اربیعین اصلی Computer Vision Project
• PKLot Computer Vision Project
• Real time Car parking Computer Vision Project
• Parking Space Computer Vision Project
• Cyclist Detection Computer Vision Project

This project contains images from the following YouTube videos: * ClearPix Camera Grocery Parking Lot * FAPS H.264 PC-DVR CCTV Security Surveillance Camera Video of Parking Lot Overview * Robbery at a hotel parking lot Caught on Surveillance camera * BLK-HDPTZ12 Security Camera Parkng Lot Surveillance Video * Empty Parking Garage | Lighting, Wind, Ventilation | 15 Minutes of Ambience * Nighttime Parking Lot Ambience (Cricket Sounds) * Clark Pacific - Coleman Highline Parking Structure 2 Time Lapse * Underground parking garage entrance

Here are a few use cases for this project:

1. Sports Analysis: Coaches and analysts can use this computer vision model to track the performance of players during a game or practice session. They can get insights about precise ball movements, successful hits, and goal rates, leading to better training and strategic decisions.

2. Highlight Generation: Sports media companies can implement the "basketball" model to automatically detect exciting moments like successful goals or impressive hits during a game. This can enable them to create instant highlights for social media, web portals, or live broadcasts, enhancing user engagement.

3. Virtual Coaching: This model can be integrated into mobile applications or websites that offer virtual basketball coaching. Users would be able to upload their videos, and the model would provide them with feedback based on their technique, ball handling, and shooting accuracy.

4. Smart Camera Systems: The "basketball" model can be embedded in smart cameras for sports facilities or courts. This would allow the cameras to follow the action as it happens, automatically zooming in on goals or exciting plays, thus enhancing the overall viewing experience for spectators.

5. Basketball Simulation Games: Game developers can utilize the model's capability to recognize various aspects of a basketball game to create more realistic and engaging basketball simulation games. The AI-driven virtual players would exhibit authentic in-game actions and responses, providing a closer-to-real gaming experience to the users.

Here are a few use cases for this project:

1. Sports Analytics and Player Performance Evaluation: The model can be used to analyze player performance in real-time or post-game, by tracking shot types, scoring efficiency, and shot angles. Coaches and analysts can use this information to understand gameplay patterns, improve player skills, and design better game strategies.

2. Broadcasting and Media Coverage Enhancement: This model can be utilized by sports broadcasters to automatically generate real-time statistics and visuals. Information such as player shot success rates, preferred shot types, and scoring techniques can be seamlessly integrated into sports broadcasts, creating an engaging viewing experience for fans.

3. Smart Camera Systems for Live Games and Training Sessions: The model can be integrated into smart camera systems to automatically track and record specific basketball events during a game or training session. For example, the system could focus on dunks or three-point shots, providing unique viewing angles and instant replays for coaching staff, commentators, and fans.

4. Video Game Development and Basketball Simulations: Game developers can use this computer vision model to create more realistic and intuitive basketball simulations. By incorporating real-world shot types, scoring techniques, and player movements, video games can provide a more authentic representation of the sport.

5. Highlights and Recap Video Creation: The model can be employed to recognize and extract key moments from basketball games, making it easier for content producers to quickly create highlights and recap videos for social media, television, or streaming platforms. This automation can save time and effort while better showcasing the most exciting moments of the game.