Computer Vision Engineer – Sports Video Analytics (Padel) About the Project We're building a video analytics platform for padel matches. A single camera captures the full court, and our ML pipeline processes the footage to detect players, track them across the match, classify shots, map positions to a 2D court minimap, and generate match statistics. The pipeline is working end-to-end. We need someone to help improve accuracy, fix edge cases, and extend capabilities — not build from scratch. What You'll Work On - Improving player identity stability — reducing false identity mints, cross-side swaps, and gallery contamination after occlusions - Tuning and extending the Re-ID pipeline (appearance embeddings, cosine distance thresholds, multi-signal fusion) - Ball tracking accuracy improvements (small/fast object detection, Kalman filtering) - Homography and camera geometry refinement (PnP, temporal smoothing, lens distortion handling) - Analyzing diagnostic logs (identity decision logs, cosine distances, track lifecycles) to diagnose tracking failures - Potentially training/fine-tuning models on padel-specific data Required Skills - Multi-Object Tracking — hands-on experience with BoT-SORT, ByteTrack, DeepSORT, or similar. You understand track-detection association, track lifecycle, and occlusion recovery - Object Detection — YOLO family (v5/v8/v11), Ultralytics ecosystem, confidence tuning, NMS - Re-Identification — appearance embeddings, gallery management, distance-based matching. Experience with OSNet, torchreid, or similar Re-ID frameworks - Computer Vision — homography computation, solvePnP, camera calibration, geometric transforms - Strong engineering skills — not just scripting. Our pipeline has state machines, registries, and fusion logic Nice-to-Have - Ball tracking experience (TrackNet, heatmap-based detection, Kalman filtering) - Pose estimation (MediaPipe, YOLO-Pose) - Sports video analytics domain experience - Experience with VLMs/multimodal models for visual understanding - Audio signal processing (onset detection, FFT) How to Stand Out in Your Proposal - Mention specific MOT or Re-ID projects you've worked on - If you've dealt with identity recovery after occlusion, tell us about it - Share any experience with sports or broadcast video analysis - Bonus: explain the difference between BoT-SORT and ByteTrack in one sentence