AI-Powered Attendance & Movement Detection System

Automation

Smart Attendance

Problem Statement

Traditional attendance systems relied on biometric or manual punch methods, leading to delays, proxy issues, and limited real-time insights. The goal was to design an automated, contactless, and intelligent system capable of:

Detecting employees at entry and exit points
Recording accurate timestamps automatically
Monitoring movement inside the premises

Proposed Solution

Our team deployed AI-enabled surveillance cameras trained for facial recognition and behavioral tracking.

Camera Integration: High-resolution cameras installed at entry/exit points capture live footage.
AI Model Training: The system identifies employees, logs their in/out times, and tracks presence within the office.
Automation Dashboard: Admins can monitor real-time data, generate reports, and analyze attendance trends without manual input.

Implementation Steps

Data Collection – High-quality wafer and chip images were collected with both defective and defect-free samples.
Model Training – A deep learning model was trained to accurately identify and classify various defect types.
System Integration – The model was deployed on the production line for real-time monitoring.
Continuous Improvement – Accuracy improved with ongoing data feedback.

Tech STack

- - - AI/ML Frameworks: TensorFlow, OpenCV
    - Backend: Python, FastAPI
    - Database: PostgreSQL
    - Hardware: IP-based AI cameras with edge computing

Results

- - 95% accuracy in attendance logging
  - Real-time employee tracking and insights
  - Fully automated attendance reports
  - Reduction in proxy or missed punches

Have a Project in mind?

Project Details

AI Automation

Service: Smart Attendance

Technologies: AI/ML Frameworks Python PostgreSQL