Hi I’m Aarav Sharma, I started this before Stardance, so this first devlog covers everything built so far (about 6 hours of work) in one go. Sideline is an AI system that films a school football or basketball match from a single fixed camera and turns that one recording into three different things: tactical analytics for coaches, event highlight reels for the school socials, and a personal highlight clip for every player, not just the goal-scorers. It runs entirely on a local laptop with no subscriptions, which matters because the commercial equivalents (Veo, Pixellot) cost hundreds up front plus monthly fees, putting them out of reach for a normal school.
The computer vision (both football and basketball). The core is one shared pipeline that detects players and the ball, tracks them across the match, works out which team each player is on, and maps the camera view onto real court coordinates so distances and positions actually mean something in metres. Getting each piece trustworthy was the bulk of the time, and a lot of it was fighting genuinely hard problems. The basketball ball tracker kept locking onto players’ heads (round, ball-sized, and constantly held right next to the ball during a shot), and I had to prove that no shape or size rule could ever separate them before fixing it with a small appearance classifier I trained on a few thousand hand-sorted image crops (head rejection went from hopeless to 98.5%). Team detection on similar kits kept collapsing into one cluster until I switched to appearance embeddings, which also caught a possession stat that was confidently pointing at the wrong team entirely.
The three deliverables. Coach analytics come out as a one-page report plus an annotated tactical video (heatmaps, distance covered, possession, formation, territory), with every number honestly labelled as either validated or derived. Event highlights are auto-detected and ranked so the best moments float to the top instead of flooding whoever is editing. Player highlights are built to be inclusive by construction: every player who steps on the court gets footage, using their on-ball moments where they have them, which is the part no commercial product bothers with and the part a school actually cares about.
The app. All of that is wrapped in a working local web app, “Sideline, Match Studio”, with a dark cinematic interface a non-technical teacher can run start to finish: upload a match, click the four court corners on a freeze-frame, pick which deliverables they want, and download the results. The backend is a FastAPI server that runs the heavy AI as background jobs (a full match takes around 3 hours, so it processes while you walk away and can survive a restart), and it wraps my existing pipeline scripts rather than rewriting them. The whole thing is tested end to end with 62 passing checks, and I have already stress-tested it on a full 47-minute match to see what breaks at real scale.
Next stop is real deployment at my school, which is the one thing I cannot fully validate until I have footage from our own courts and teams.