Create AI CM:sport task

This algorithm allows to identify various sporting activities in a video. Generic info about all capabilities and limits see in the generic “Content Moderation” method. What is Sports activity detection? Sports activity detection by AI involves using machine learning and computer vision technologies to automatically identify, analyze, and interpret various activities within sports and generic videos. This can include detecting specific types, actions, events, and moments. This model operates on a video sequence (and not on images as most of the used computer vision models). Make sure your video is at least 10-15 seconds long. Sports activities can be detected:

• archery
• arm wrestling
• playing badminton
• playing baseball
• basketball dunk
• bowling
• boxing punch
• boxing speed bag
• catching or throwing baseball
• catching or throwing softball
• cricket
• curling
• disc golfing
• dodgeball
• fencing
• football
• golf chipping
• golf driving
• golf putting
• hitting baseball
• hockey stop
• ice skating
• javelin throw
• juggling soccer ball
• kayaking
• kicking field goal
• kicking soccer ball
• playing cricket
• playing field hockey
• playing ice hockey
• playing kickball
• playing lacrosse
• playing ping pong
• playing polo
• playing squash or racquetball
• playing tennis
• playing volleyball
• pole vault
• riding a bike
• riding or walking with horse
• roller skating
• rowing
• sailing
• shooting goal (soccer)
• skateboarding
• skiing Use cases:
• Sports leagues and content creators can use AI to monitor UGC for unauthorized publications of their content. This can include detecting specific sporting events or activities that are part of copyrighted content.
• Sports fans often miss live games and rely on highlight reels. AI can automatically detect key moments like goals, touchdowns, or game-winning shots in uploaded UGC videos and compile them into personalized highlight reels. How to use? The information is returned with the video frame number where it was found and probability of the detected activty. Identification is done using AI, so for each activity a probability percentage is applied; activities with a probability of at least 30% are included in the response. Video processing speed is approximately 1:5. Example of detected sports activity:

{
"`sport_detected`": true,
"`detection_results`": [ "shooting goal (soccer)" ],
"frames": [
{
"label": "shooting goal (soccer)",
"`frame_number`": 98,
"confidence": 0.99
},...
]
}

Example response when sports activities were not found:

{
"`sport_detected`": false,
"`detection_results`": []
"frames": []
}

Please note that the API only provides a set of data (json) about the objects found, so no video is generated. The demo video video (above ^) was specially created based on json from the API for visual demonstration and better perception of the possibilities.

Authorizations

Authorization · string · header · required

API key for authentication. Make sure to include the word apikey, followed by a single space and then your token. Example: apikey 1234$abcdef

Body

application/json

category · enum<string> · required

AI content moderation with types of sports activity detection Model for analysis (content-moderation only). Determines what exactly needs to be found in the video.

Available options:

sport

task_name · enum<string> · required

Name of the task to be performed

Available options:

content-moderation

url · string · required

URL to the MP4 file to analyse. File must be publicly accessible via HTTP/HTTPS.

client_user_id · string

Meta parameter, designed to store your own identifier. Can be used by you to tag requests from different end-users. It is not used in any way in video processing.

Maximum length: 256

client_entity_data · string

Meta parameter, designed to store your own extra information about a video entity: video source, video id, etc. It is not used in any way in video processing. For example, if an AI-task was created automatically when you uploaded a video with the AI auto-processing option (nudity detection, etc), then the ID of the associated video for which the task was performed will be explicitly indicated here.

Maximum length: 4096

Response

201

application/json

Response returns ID of the created AI task. Using this AI task ID, you can check the status and get the video processing result. Look at GET /ai/results method.

task_id · string<uuid> · required

ID of the created AI task, from which you can get the execution result