Shoplifting Computer Vision Project
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Shoplifting Detection Project Description
Project Overview
The Shoplifting Detection project aims to develop a real-time system to detect shoplifting using video surveillance. By employing Object Detection techniques, the system will identify and monitor individuals and items within a store to recognize potential shoplifting behaviors.
Objectives
- Real-Time Detection: Analyze live video feeds to detect suspicious activities.
- Object Identification: Identify and track individuals and items.
- Alert Generation: Notify security personnel of potential shoplifting incidents.
Key Components
- Data Collection and Annotation: Gather and label video footage from store cameras.
- Model Training: Use pre-trained Object Detection models (e.g., YOLO, SSD) and fine-tune with annotated data.
- System Integration: Implement the model in a real-time video analysis pipeline.
- Alert Mechanism: Develop a notification system for security alerts.
Technical Stack
- Programming Languages: Python
- Libraries: TensorFlow, OpenCV
- Models: YOLO, SSD
Team Members
- Muhammad Abdullah
- Abdullah Basar
- Malghalara Ahmad
- Roha Areej
This project is being conducted during our internship at Codic Solutions. Our goal is to improve retail security and reduce losses due to shoplifting through advanced video analysis technology.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
shoplifting-q4ppm_dataset,
title = { Shoplifting Dataset },
type = { Open Source Dataset },
author = { Shoplifting },
howpublished = { \url{ https://universe.roboflow.com/shoplifting-pxuzc/shoplifting-q4ppm } },
url = { https://universe.roboflow.com/shoplifting-pxuzc/shoplifting-q4ppm },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { aug },
note = { visited on 2024-09-19 },
}