TTNet Demo
Details
- OpenTTGames Dataset (Full-HD videos, 120 fps)
- Multi-Task : Ball detection, Semantic segmentation, Events spotting
- Ball size : about 15 pixels on average (color : Magenta)
- Segmentation : humans(G), table(B) and scoreboard(R) classes with channel-wise encoding
- Events : "bounce" and "over net"
- Input : downscaled video frames (320px,128px) and 9 frame sequences (less than 0.1s in real time)
- Annotation : Event - center frame, Ball position and seg mask - last frame, respectively
- Inference capability : more than 166 fps on a machine with a single NVIDIA RTX 2080Ti GPU
A demo (60 fps) by a trained model (loss condition : Unbalanced) using following repository: https://github.com/maudzung/TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch
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This model is almost robust for time inversion input.
Input videos from YouTube (60 fps)
- Ball detection : moderate, Segmentation & Event : terrible
- Clip video scene from 1:18 (link)
- ref. download video : pytube.ipynb
