SportsVision — AI-Powered Sports Video Analytics Platform
SportsVision is a production-ready AI SaaS platform that converts raw sports match footage into structured, actionable insights using real-time computer vision and deep learning.
Project Overview
Manual sports video analysis is slow, subjective, and resource-intensive. Coaches often spend hours scrubbing through footage to identify key moments, player positions, and tactical patterns. SportsVision replaces this manual process with a fully automated, AI-driven pipeline that analyzes sports match footage frame-by-frame. Using multiple specialized deep learning models, the platform simultaneously tracks the ball trajectory, detects players, recognizes game actions, and segments the court. The output is a richly annotated video combined with structured performance data that coaches and analysts can immediately act upon.
System Architecture
SportsVision is built using a layered microservices architecture designed for scalability, modularity, and future extensibility. The React frontend communicates with a FastAPI backend via REST APIs. The backend exposes orchestration endpoints that manage video ingestion, frame extraction, inference scheduling, and output rendering. Each machine learning capability is encapsulated in an isolated service, allowing independent upgrades and experimentation without breaking the pipeline.
Ball Tracking Module
Hybrid pipeline using YOLOv7 for ball detection combined with DaSiamRPN for temporal tracking, enabling smooth trajectory reconstruction even during occlusions and fast spikes.
Player Detection Module
YOLOv8-based object detection model optimized for indoor court environments, providing real-time bounding boxes for all players on the court.
Action Recognition Engine
Custom-trained YOLOv8 classifier that identifies sports-specific actions such as spike, block, serve, set, and defensive dig.
Court Segmentation Service
RoboFlow-powered segmentation model that detects court boundaries and key zones, cached to reduce repeated inference calls.
Pipeline Orchestrator
Central controller that coordinates frame extraction, model execution order, async inference, and annotated frame composition.
Implementation Details
Code Example
class MLOrchestrator:\n def __init__(self):\n self.ball_tracker = BallTrackingService()\n self.player_detector = PlayerDetectionService()\n self.court_detector = CourtDetectionService()\n self.action_recognizer = ActionRecognitionService()\n\n async def process_video(self, video_path: str, stages: list):\n frames = extract_frames(video_path)\n cached_court = None\n\n for frame in frames:\n if 'court' in stages and cached_court is None:\n cached_court = self.court_detector.segment(frame)\n if 'ball' in stages:\n frame = self.ball_tracker.detect(frame)\n if 'players' in stages:\n frame = self.player_detector.detect(frame)\n if 'actions' in stages:\n frame = self.action_recognizer.classify(frame)\n\n frame = overlay_annotations(frame, cached_court)\n yield frameAgent Memory
Because sports courts remain static throughout a match, court segmentation is performed only on initial frames. The result is cached and reused, reducing external API calls by 95%, lowering costs, and improving throughput consistency.
Workflow
Video Upload — Users upload raw match footage through a responsive drag-and-drop interface.\n2. Model Selection — Users choose which AI modules to enable based on their analysis needs.\n3. Job Queuing — Backend queues the video processing job and allocates compute resources.\n4. Frame-by-Frame Inference — Each frame passes through selected AI models.\n5. Annotation Rendering — Bounding boxes, trajectories, labels, and court overlays are drawn.\n6. Output Delivery — Final HD annotated video is streamed back for preview and download.
Results & Impact
"SportsVision fundamentally changed our analysis workflow. Coaches can now focus on strategy instead of manual video breakdown."
Time Efficiency
Reduced manual video analysis from several hours to a few minutes per match.
High Accuracy
Achieved 98.5% ball tracking accuracy across different lighting and camera angles.
Tactical Insights
Automatically highlights key actions and patterns for performance review.
Scalable Deployment
Cloud-native design supports multiple concurrent users and large video workloads.
About the Author
Shubham Rathod
AI Context Engineer
Apex Neural
Shubham is an AI Context Engineer specializing in end-to-end agentic AI systems and full-stack SaaS development. He has hands-on experience across the complete AI lifecycle, including data preprocessing, model building, deployment, and monitoring. His expertise spans computer vision, deep learning, automation workflows, and LLM-powered tools.
Contributors
Sunny
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