Real Time Entry-EXIT Occupancy Tracker using Python and computer vision YOLOV8

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Real Time Entry-EXIT Occupancy Tracker using Python and computer vision YOLOV8

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1.Abstract

The project titled “Real Time Entry-Exit Occupancy Tracker using Python and Computer Vision with YOLOv8” is designed to automatically monitor and analyze the number of people entering and exiting a defined area in real time. The system aims to provide an efficient and accurate solution for people counting, also known as footfall tracking, which plays a crucial role in various domains such as retail analytics, safety compliance, and operational management.

In retail environments, understanding customer footfall is essential for improving business performance. By analyzing hourly and daily entry-exit data, store managers can optimize staff allocation, improve customer service, and enhance overall operational efficiency. Additionally, occupancy tracking ensures safety compliance by maintaining the maximum allowed number of individuals within a specific space based on area regulations.

The proposed system uses Python programming language along with computer vision techniques and the YOLOv8 (You Only Look Once version 8) object detection model to detect and track humans in real time. The YOLOv8 model is trained to accurately identify persons in video frames captured through CCTV or webcam sources. A tracking algorithm is applied to avoid duplicate counting and to differentiate between entry and exit movements.

The system processes live video streams, detects individuals, and updates occupancy counts dynamically. When predefined occupancy thresholds are exceeded, alerts can be generated for safety monitoring. This makes the system suitable for applications in shopping malls, retail stores, offices, public buildings, and restricted zones.

Overall, this project provides a smart, scalable, and automated solution for real-time occupancy tracking, helping businesses improve decision-making while ensuring safety and regulatory compliance.

2. Objectives

To develop a real-time people counting system using YOLOv8.
To track entry and exit of individuals accurately.
To calculate occupancy levels in a given area.
To assist retail businesses in analyzing footfall data.
To improve operational efficiency and staffing decisions.
To ensure safety compliance by avoiding overcrowding.

3.Existing System

• In traditional people counting systems, basic computer vision techniques such as background subtraction, frame differencing, and motion detection are used to detect and count individuals in a monitored area.

• Some systems use fixed CCTV cameras with manual monitoring to estimate occupancy levels in real time.

• A few advanced systems use classical machine learning methods combined with feature extraction techniques for detecting human presence and movement direction.

• Existing systems mainly focus on simple detection and counting without advanced tracking capabilities for maintaining unique identities of individuals.

4.Proposed System

• The proposed system implements a real-time occupancy tracking system using Python and YOLOv8 deep learning model.

• YOLOv8 is used for accurate and fast detection of persons in live video streams.

• A tracking algorithm is integrated to maintain unique identity of each person and avoid duplicate counting.

• Entry and exit lines are defined to monitor movement direction and calculate occupancy count.

• The system processes live CCTV or webcam feeds to provide real-time analytics.

5. Implementation Procedure

Module 1: Environment Setup

Install Python and required libraries.
Install YOLOv8 framework and dependencies.

Module 2: Video Input Processing

Capture video from CCTV or webcam.
Preprocess frames for detection.

Module 3: Object Detection

Use YOLOv8 to detect “person” class.
Generate bounding boxes around individuals.

Module 4: Object Tracking

Assign unique IDs to detected persons.
Track movement across frames.

Module 5: Entry–Exit Logic

Define a virtual line or region.
Count people crossing the line:
Increment count for entry.
Decrement count for exit.

Module 6: Occupancy Calculation

Maintain real-time count of people inside the area.
Store data for analysis.

Module 7: Visualization & Output

Display live video with bounding boxes and counts.
Show entry, exit, and occupancy numbers.

6.Software Requirements

Operating System : Windows 10 / Linux

Programming Language : Python

Libraries : OpenCV, NumPy, Ultralytics YOLOv8

Framework : Deep Learning (YOLOv8)

IDE : VS Code / PyCharm

7.Hardware Requirements

Processor : Intel i3 or higher (i5 recommended)

RAM : 4 GB minimum (8 GB recommended)

Hard Disk : 500 GB

Camera : Webcam / CCTV camera

GPU : Optional (for faster processing)

8. Advantages of the Project

Accurate real-time people counting.
Fully automated system (no manual effort).
Useful for retail analytics and decision-making.
Helps maintain safety compliance.
Scalable for large environments.
Provides valuable business insights (footfall & conversion).

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