I need a computer-vision pipeline that can ingest an uploaded logo image (PNG or JPG) together with a video file and then return every instance of that logo throughout the clip. The videos that come in will be MP4, AVI, or MOV, and the solution must run smoothly. Here’s how I picture the workflow: • The script accepts the logo and video paths from a simple CLI or minimal API endpoint. • It scans each frame, flags every logo hit, and outputs a tidy JSON or CSV that lists, for each detection, the start timestamp, on-screen duration, and bounding-box coordinates. • No extra training or fine-tuning whenever a new logo is supplied—the detector should work right away with whatever image is uploaded. I’m after well-commented, maintainable code, and I’ll need brief setup instructions so I can reproduce your results locally (Python virtual-env, requirements.txt, maybe a Dockerfile). Fast turnaround is important—I’d like to start reviewing working code as soon as possible. If you’ve already built anything similar—logo finders, object matchers, ORB/SIFT/SURF pipelines, YOLO or Faster-RCNN tweaks—tell me about it. Strong communication during the build is a must, and please start your reply with “SPORTS” so I know the details above were read.