Event-AHU/HARDVS
[AAAI-2024] HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
This project helps classify human activities using specialized event cameras instead of traditional RGB cameras. It takes raw event stream data or event-based images as input and identifies specific actions, overcoming challenges like poor lighting or privacy concerns. It's designed for researchers and engineers working with event cameras in areas like surveillance, elderly care monitoring, or sports analysis.
Use this if you need to recognize human activities from event camera footage, especially in challenging environments or when privacy is a concern.
Not ideal if your primary data source is standard RGB video and you don't have access to event camera systems.
Stars
56
Forks
4
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 03, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/Event-AHU/HARDVS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
quic/sense
Enhance your application with the ability to see and interact with humans using any RGB camera.
AlexanderMelde/SPHAR-Dataset
Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes,...
CV-ZMH/human-action-recognition
Multi Person Skeleton Based Action Recognition and Tracking
mmact19/2019
MMAct: A Large-Scale Dataset for Cross Modal Learning on Human Action Understanding
yujmo/CZU_MHAD
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10...