https://github.com/aaditagarwal/activenet
A pipeline which can detect levels of activeness in real-time, using a single RGB image of a target person
https://github.com/aaditagarwal/activenet
computer-vision machine-learning notification-alert openpose pose-estimation
Last synced: 11 months ago
JSON representation
A pipeline which can detect levels of activeness in real-time, using a single RGB image of a target person
- Host: GitHub
- URL: https://github.com/aaditagarwal/activenet
- Owner: aaditagarwal
- Created: 2020-08-21T19:15:22.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-08-22T07:17:38.000Z (almost 6 years ago)
- Last Synced: 2025-01-16T16:23:34.474Z (over 1 year ago)
- Topics: computer-vision, machine-learning, notification-alert, openpose, pose-estimation
- Language: Python
- Homepage:
- Size: 74.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ActiveNet
## Abstract
Our work builds on the idea to formulate a pipeline which can detect levels of activeness in real-time, using a single RGB image of a target person. It expands the aim to create a generalized solution which works under any/most configurations, be it in an interview, online class, security surveillance, et cetera.\
We introduce a novel pose encoding technique, which encodes the 2-Dimensional keypoints extracted using Human Pose Estimation (HPE) algorithm.\
Our alerting mechanism is wrapped around the whole approach; it provides a solution to inhibit low-activeness by sending notification alerts to individuals involved.
##### ActiveNet Multi-Stage Mechanism
##### Alert Mechanism
## Hardware Requirements
The pipeline can be run on a CPU, as well as on a dedicated GPU. We recommend using a dedicated GPU to achieve our framerate of ~35fps with a single Nvidia GeForce GTX 1650 graphics card.
## Dependencies Required
1. Anaconda
2. Python3
3. PyTorch
4. scikit-learn
5. OpenCV
NOTE: Dependencies can either be installed individually, or a GPU enabled Anaconda environment can be created from the environment file using the following instructions:
## Execution Instructions
```
conda env create -f ActiveNet_Environment.yml
conda activate ActiveNet
python demo.py --source
```
NOTE: To run the demo on CPU, add extra flag --cpu to the above command.
#### Read [SLACK_WORKSPACE.md](SLACK_WORKSPACE.md) for information regarding the incoming webhooks.
## Screenshots
##### Above 75% Activeness Level Prediction
##### Between 50% and 75% Activeness Level Prediction
##### Between 25% and 50% Activeness Level Prediction
##### Below 25% Activeness Level Prediction
##### Notification Alert for Below 25% Activeness Level on Desktop
##### Notification Alert for Below 25% Activeness Level on Mobile Device
## Contributors
1. [Aitik Gupta](https://github.com/aitikgupta)\
ABV-IIITM, Gwalior\
aitikgupta@gmail.com
2. [Aadit Agarwal](https://github.com/aaditagarwal/)\
ABV-IIITM, Gwalior\
agarwal.aadit99@gmail.com