Automated Sports Video Analytics Pipeline
Budget: ₹12,500 – ₹37,500 INR
I need a robust frame-analysis pipeline that can ingest raw, unsynchronised multi-camera footage from a football match, stitch the feeds together, and act as the central engine for every downstream insight we want to surface. The very first sport you will tackle is Football, but I want the parameters, event types, and metric catalogue structured so that the same codebase can be extended later to basketball, hockey, and any other field-based sport without rewriting core logic.
Core objectives
• Multi-camera alignment, calibration, and frame syncing
• Real-time player and ball detection / tracking (YOLO, DeepSort, OpenPose, or comparable frameworks)
• Automated event recognition so the system can compile full highlight reels—the highest visual priority right now—alongside goal-only, defensive-moment, and candid stills without manual editing (FFmpeg, OpenCV for the assembly pipeline)
• Per-player stat extraction focused first on Distance Covered, then scaling out to shots, positional heatmaps, and other movement-based metrics
• Generation of derived performance scores, fitness indices, and similar composite analytics built on the raw stats
• Clean API or micro-service endpoints so downstream apps can query clips, stats, and ratings
Acceptance criteria
1. Feed the system 90 minutes of multi-camera football footage; receive an automatically cut highlight reel in standard HD format.
2. CSV/JSON output that lists each player’s total distance covered with a timestamped breakdown.
3. Clear configuration file showing how event labels, pitch geometry, and stat thresholds can be swapped for another sport without touching the core code.
4. Documentation that explains model choices, data flow, and how a developer can add new metrics or visual templates.
If this challenge excites you and you have proven experience with computer vision (OpenCV, PyTorch/TensorFlow), sports analytics, and efficient video processing, let’s talk through your approach and timeline for an MVP.
Core objectives
• Multi-camera alignment, calibration, and frame syncing
• Real-time player and ball detection / tracking (YOLO, DeepSort, OpenPose, or comparable frameworks)
• Automated event recognition so the system can compile full highlight reels—the highest visual priority right now—alongside goal-only, defensive-moment, and candid stills without manual editing (FFmpeg, OpenCV for the assembly pipeline)
• Per-player stat extraction focused first on Distance Covered, then scaling out to shots, positional heatmaps, and other movement-based metrics
• Generation of derived performance scores, fitness indices, and similar composite analytics built on the raw stats
• Clean API or micro-service endpoints so downstream apps can query clips, stats, and ratings
Acceptance criteria
1. Feed the system 90 minutes of multi-camera football footage; receive an automatically cut highlight reel in standard HD format.
2. CSV/JSON output that lists each player’s total distance covered with a timestamped breakdown.
3. Clear configuration file showing how event labels, pitch geometry, and stat thresholds can be swapped for another sport without touching the core code.
4. Documentation that explains model choices, data flow, and how a developer can add new metrics or visual templates.
If this challenge excites you and you have proven experience with computer vision (OpenCV, PyTorch/TensorFlow), sports analytics, and efficient video processing, let’s talk through your approach and timeline for an MVP.