Sh-31/Group-Activity-Recognition
A modern implementation of CVPR 2016 paper: "A Hierarchical Deep Temporal Model for Group Activity Recognition."
This tool analyzes video footage to automatically identify and classify complex group activities and individual player actions, initially trained on volleyball games. It takes video frames as input and outputs labels for actions like "Left Spike" or "Player Digging." Sports analysts, coaches, and researchers in human behavior would find this useful for detailed activity logging and performance analysis.
No commits in the last 6 months.
Use this if you need to automatically recognize and label specific group activities and individual actions within video footage for detailed analysis.
Not ideal if you require real-time recognition for live applications or if your primary focus is on object detection rather than activity classification.
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15
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Language
Python
License
BSD-2-Clause
Category
Last pushed
Feb 24, 2025
Commits (30d)
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