AI-Based Beach Volleyball Video Analysis
Budget: €5,000 – €10,000 EUR
AI Video Analysis for Beach Volleyball – Project Request
We are developing a digital project in the beach volleyball space, with the goal of building a platform for video analysis, scouting, and performance insights.
We are looking for an expert or team capable of developing (or evaluating the development of) an AI-based system that can automatically analyze beach volleyball match videos and generate structured data and statistics.
At this stage, our goal is to understand what is realistically achievable, in terms of functionality, accuracy, timelines, and cost.
Project Context
We already have access to a growing library of beach volleyball match videos, recorded from real tournaments.
These videos are typically recorded with a fixed or semi-fixed camera and show the full court or most of it.
Over time, this video database will continue to grow significantly.
Our long-term goal is to build a proprietary platform where users (players, coaches, scouts, clubs) can analyze matches and extract meaningful insights.
Main Objective
The system should be able to take a beach volleyball match video as input and automatically generate structured information about the match.
This includes:
identifying players
identifying teams
detecting game events
generating player statistics
generating team statistics
understanding direction of play
building a timeline of the match
The final output should be usable within a digital platform (e.g. JSON or database-ready format).
Required Capabilities
1. Court and Game Understanding
The system should understand the structure of a beach volleyball court and the game context, including:
identifying the court
distinguishing the two sides
understanding which team is on each side at a given moment
interpreting the direction of play
2. Player Detection and Tracking
The system should detect and track the 4 players on the court (2 per team).
Requirements:
continuous detection of players
ability to distinguish between the two teams
tracking players during rallies
maintaining player identity even in case of:
player overlap or crossing
partial occlusions
fast movements
temporary loss of visibility
It is acceptable to manually assign player identities at the beginning (e.g. Player 1, Player 2, etc.), as long as the system can maintain consistent tracking afterward.
3. Side Switching (Critical Requirement)
In beach volleyball, teams switch sides of the court multiple times during a match (typically every few points, depending on the format).
This creates a critical challenge:
players do not remain on the same side of the court throughout the match;
the same team can appear on both sides at different times;
position alone cannot be used to identify players or teams.
The system must:
maintain consistent player identity even after side switches;
correctly assign actions and statistics to the right player regardless of court side;
avoid confusion between teams when side switching occurs;
handle multiple side switches within the same match.
This is a key requirement for the correctness of the analysis.
4. Event Detection
The system should identify the main events of a beach volleyball match.
Target events include:
serve
reception
set
attack
block
defense
ace
service error
attack winner
attack error (out or net)
blocked attack
point awarded
A progressive approach is acceptable, starting from basic events and expanding to more detailed recognition.
5. Player Statistics
The system should generate individual statistics for each player, such as:
number of serves
aces
service errors
attack attempts
attack winners
attack errors
blocked attacks
blocks (points)
defensive actions
total points
All statistics must remain correctly associated with the same player throughout the match, even after side switches.
6. Attack Direction Analysis
An important feature is the ability to analyze the direction of attacks.
Examples include:
cross-court (diagonal)
line
short shot (tip)
deep shot
attack to a specific area of the court
attack targeting a specific player
attack to open space
This is highly relevant for scouting and tactical analysis.
7. Ball Tracking
If possible, the system should track the ball during play.
We are aware this is a complex task due to:
ball speed
small object size
outdoor conditions
variable lighting
non-professional video quality
Partial or approximate solutions are also acceptable.
8. Structured Data Output
The system should produce structured output that can be integrated into a platform.
Preferred format:
JSON or equivalent structured data
The output should include:
players and team assignment
timeline of events with timestamps
event type
involved player
outcome of the action
direction of play (if applicable)
aggregated statistics
confidence level for detected events
9. Semi-Automated Workflow (AI + Human Review)
A semi-automated approach is acceptable and desirable, especially in early stages.
Expected workflow:
AI analyzes the video
AI generates events and statistics
a human operator can review and correct the output
corrections are stored and can be used to improve the system over time
Final Goal
The long-term goal is to build a scalable and proprietary system for beach volleyball video analysis.
We are not expecting a perfect solution from the beginning, but a system that can:
start with basic capabilities
improve over time
leverage growing video data
evolve toward more advanced analysis
We are developing a digital project in the beach volleyball space, with the goal of building a platform for video analysis, scouting, and performance insights.
We are looking for an expert or team capable of developing (or evaluating the development of) an AI-based system that can automatically analyze beach volleyball match videos and generate structured data and statistics.
At this stage, our goal is to understand what is realistically achievable, in terms of functionality, accuracy, timelines, and cost.
Project Context
We already have access to a growing library of beach volleyball match videos, recorded from real tournaments.
These videos are typically recorded with a fixed or semi-fixed camera and show the full court or most of it.
Over time, this video database will continue to grow significantly.
Our long-term goal is to build a proprietary platform where users (players, coaches, scouts, clubs) can analyze matches and extract meaningful insights.
Main Objective
The system should be able to take a beach volleyball match video as input and automatically generate structured information about the match.
This includes:
identifying players
identifying teams
detecting game events
generating player statistics
generating team statistics
understanding direction of play
building a timeline of the match
The final output should be usable within a digital platform (e.g. JSON or database-ready format).
Required Capabilities
1. Court and Game Understanding
The system should understand the structure of a beach volleyball court and the game context, including:
identifying the court
distinguishing the two sides
understanding which team is on each side at a given moment
interpreting the direction of play
2. Player Detection and Tracking
The system should detect and track the 4 players on the court (2 per team).
Requirements:
continuous detection of players
ability to distinguish between the two teams
tracking players during rallies
maintaining player identity even in case of:
player overlap or crossing
partial occlusions
fast movements
temporary loss of visibility
It is acceptable to manually assign player identities at the beginning (e.g. Player 1, Player 2, etc.), as long as the system can maintain consistent tracking afterward.
3. Side Switching (Critical Requirement)
In beach volleyball, teams switch sides of the court multiple times during a match (typically every few points, depending on the format).
This creates a critical challenge:
players do not remain on the same side of the court throughout the match;
the same team can appear on both sides at different times;
position alone cannot be used to identify players or teams.
The system must:
maintain consistent player identity even after side switches;
correctly assign actions and statistics to the right player regardless of court side;
avoid confusion between teams when side switching occurs;
handle multiple side switches within the same match.
This is a key requirement for the correctness of the analysis.
4. Event Detection
The system should identify the main events of a beach volleyball match.
Target events include:
serve
reception
set
attack
block
defense
ace
service error
attack winner
attack error (out or net)
blocked attack
point awarded
A progressive approach is acceptable, starting from basic events and expanding to more detailed recognition.
5. Player Statistics
The system should generate individual statistics for each player, such as:
number of serves
aces
service errors
attack attempts
attack winners
attack errors
blocked attacks
blocks (points)
defensive actions
total points
All statistics must remain correctly associated with the same player throughout the match, even after side switches.
6. Attack Direction Analysis
An important feature is the ability to analyze the direction of attacks.
Examples include:
cross-court (diagonal)
line
short shot (tip)
deep shot
attack to a specific area of the court
attack targeting a specific player
attack to open space
This is highly relevant for scouting and tactical analysis.
7. Ball Tracking
If possible, the system should track the ball during play.
We are aware this is a complex task due to:
ball speed
small object size
outdoor conditions
variable lighting
non-professional video quality
Partial or approximate solutions are also acceptable.
8. Structured Data Output
The system should produce structured output that can be integrated into a platform.
Preferred format:
JSON or equivalent structured data
The output should include:
players and team assignment
timeline of events with timestamps
event type
involved player
outcome of the action
direction of play (if applicable)
aggregated statistics
confidence level for detected events
9. Semi-Automated Workflow (AI + Human Review)
A semi-automated approach is acceptable and desirable, especially in early stages.
Expected workflow:
AI analyzes the video
AI generates events and statistics
a human operator can review and correct the output
corrections are stored and can be used to improve the system over time
Final Goal
The long-term goal is to build a scalable and proprietary system for beach volleyball video analysis.
We are not expecting a perfect solution from the beginning, but a system that can:
start with basic capabilities
improve over time
leverage growing video data
evolve toward more advanced analysis