- 1 min

Table of Contents:

ActiveNet is a multi-stage mechanism to detect levels of activeness, with a single monocular image input of a target person.

In-Depth Details

Our paper, ActiveNet: A computer-vision based approach to determine lethargy consists of in-depth details, literature review, experimentation results, etc. It can be found on arXiv.

Implementation Details


Multi-stage flowchart of ActiveNet

Alert mechanism flowchart


Human Pose Estimation

Modified this implementation to interpolate 2 extra keypoints from ground-truth 17 keypoints, used their weights trained on MSCOCO dataset.

Pose Encoding

This module creates the angular encodings from bare keypoint annotations from HPE module.

Multi-Class Classification

Level of activeness assessed into 1 of 4 levels with a Decision-Tree based classifier, trained on a self-scraped dataset.

Levels of Activeness

Alarm System

Low activeness alerts run out to users of a Slack Workspace.


Our paper has been accepted in ACM India Joint International Conference on Data Science and Management of Data, (CoDS-COMAD ‘21)!
The whole pipeline is open-sourced on GitHub.

Aitik Gupta

I go by @aitikgupta throughout the web! \o/

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