> ## Documentation Index > Fetch the complete documentation index at: https://gcore.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # 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"](/docs/api-reference/streaming/ai/create-ai-task) 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. ![AI Content Moderation: sports activity detection visual example](https://demo-files.gvideo.io/apidocs/sports_football_detection.gif) **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. ## OpenAPI ````yaml /api-reference/services_documented/streaming_api.yaml post /streaming/ai/tasks#cm_sport openapi: 3.1.0 info: title: Gcore OpenAPI – Streaming API description: >- This OpenAPI is an aggregated OpenAPI specification that unifies all Gcore products into a single file. It covers Cloud, CDN, DNS, WAAP, DDoS Protection, Object Storage, Streaming, and FastEdge services. version: '2026-05-11T15:10:30.328297+00:00' servers: - url: https://api.gcore.com security: - APIKey: [] tags: - name: AI x-displayName: AI - name: Broadcasts x-displayName: Broadcasts - name: Directories x-displayName: Directories - name: Overlays x-displayName: Overlays - name: Players x-displayName: Players - name: Playlists x-displayName: Playlists - name: QualitySets x-displayName: QualitySets - name: Restreams x-displayName: Restreams - name: Streaming Statistics x-displayName: Statistics - name: Streams x-displayName: Streams - name: Subtitles x-displayName: Subtitles - name: Videos x-displayName: Videos paths: /streaming/ai/tasks#cm_sport: post: tags: - AI summary: Create AI CM:sport task description: >- This algorithm allows to identify various sporting activities in a video. Generic info about all capabilities and limits see in the generic ["Content Moderation"](/docs/api-reference/streaming/ai/create-ai-task) 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. ![AI Content Moderation: sports activity detection visual example](https://demo-files.gvideo.io/apidocs/sports_football_detection.gif) **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. operationId: post_ai_contentmoderation_sport parameters: [] requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/ai_contentmoderation_sport' responses: '201': description: >- 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. content: application/json: schema: $ref: '#/components/schemas/ai_post_response' '400': description: |- Bad request: - "url" is not specified, - Queue limit reached (100), try later, - etc content: application/json: schema: $ref: '#/components/schemas/streaming_error' '422': description: >- This is advanced functionality; to enable it, contact your manager or the Support Team. content: application/json: schema: $ref: '#/components/schemas/upgraderequired' components: schemas: ai_contentmoderation_sport: allOf: - $ref: '#/components/schemas/ai_contentmoderation_general' required: - category properties: category: type: string enum: - sport description: AI content moderation with types of sports activity detection example: url: >- https://demo-files.gvideo.io/ai_demo_soccer_players_passing_the_ball.mp4 task_name: content-moderation category: sport ai_post_response: type: object required: - task_id properties: task_id: type: string format: uuid description: >- ID of the created AI task, from which you can get the execution result example: task_id: aafe70c6-0000-0000-0000-327b65f7670f streaming_error: type: object properties: error: type: string description: Text message with description of error. example: error: Queue limit reached (100), try later. upgraderequired: type: object properties: error: type: string description: >- This is advanced functionality; to enable it, contact your manager or support service. example: error: Feature is disabled. Contact support to enable. ai_contentmoderation_general: type: object required: - url - task_name properties: task_name: type: string description: Name of the task to be performed enum: - content-moderation url: type: string description: >- URL to the MP4 file to analyse. File must be publicly accessible via HTTP/HTTPS. category: type: string description: >- Model for analysis (content-moderation only). Determines what exactly needs to be found in the video. enum: - sport - nsfw - hard_nudity - soft_nudity client_user_id: type: string maxLength: 256 default: null description: >- 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. client_entity_data: type: string maxLength: 4096 default: null description: >- 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. example: url: https://demo-files.gvideo.io/ai_demo_subtitles_nudity_detection.mp4 task_name: content-moderation category: nsfw securitySchemes: APIKey: description: >- API key for authentication. Make sure to include the word `apikey`, followed by a single space and then your token. Example: `apikey 1234$abcdef` type: apiKey in: header name: Authorization ````