Shoplifting Activity Detection

-20%

Shoplifting Activity Detection

0 Orders 0 Wish listed

₹4,999.00

Qty

Total price:

₹4,999.00

Overview
Reviews

Detail Description

1. Abstract

Shoplifting Activity Detection is an AI-based surveillance project designed to analyze video footage and identify potentially suspicious activities in retail environments. The system uses computer vision and deep learning techniques to monitor customer movements, recognize activity patterns, and flag events that may require further review. It can support security personnel in supermarkets, shopping malls, and retail stores by generating alerts and helping them review relevant video footage. The system is intended as a decision-support tool rather than an automatic determination that theft has occurred.

2. Objectives

Monitor retail environments using video surveillance.
Analyze customer movements and activity patterns.
Identify potentially suspicious actions for further review.
Generate alerts when predefined activity patterns are detected.
Store event timestamps and relevant video frames for review.
Support retail security teams in monitoring store activity.

3. Existing System

Traditional shoplifting prevention relies on security guards, CCTV operators, and manual inspection of recorded footage. These methods can be time-consuming, particularly in large retail stores with many customers and cameras.

3.1 Disadvantages

Requires continuous manual monitoring.
Reviewing large amounts of footage takes time.
Suspicious activities may be missed in crowded environments.
Basic motion detection cannot understand complex actions.
Human judgment may be inconsistent when monitoring multiple camera feeds.

4. Proposed System

The proposed system uses computer vision, object tracking, and activity recognition to analyze retail surveillance footage. It identifies predefined movement or interaction patterns that may warrant review and generates an alert containing the event time and relevant video frame. A security professional reviews each alert before deciding whether any further action is appropriate.

5. Implementation Procedure

Collect suitable retail surveillance videos or approved sample datasets.
Load and preprocess video frames using Python and OpenCV.
Detect people and relevant objects using a compatible object detection model.
Track individuals and objects across video frames.
Apply activity recognition or rule-based analysis to identify predefined suspicious patterns.
Generate alerts when activity meets configured detection criteria.
Display event timestamps and relevant frames on a dashboard.
Evaluate system performance using test videos and labeled examples.

6. Software Requirements

Programming Language: Python
Computer Vision Library: OpenCV
Deep Learning Framework: PyTorch or TensorFlow
Object Detection: YOLO or another compatible model
Data Processing: NumPy
Development Environment: VS Code, PyCharm, or Jupyter Notebook
Optional Web Interface: Flask or Streamlit
Input Source: Recorded video or compatible camera stream

7. Hardware Requirements

Processor: Intel Core i5 or equivalent recommended
RAM: Minimum 8 GB recommended
Storage: SSD with sufficient space for datasets and video recordings
Camera: Webcam or compatible surveillance camera
GPU: Optional, but recommended for faster deep-learning inference
Display: Standard monitor

8. Advantages of the Project

Supports automated analysis of surveillance footage.
Helps security teams prioritize video segments for review.
Reduces the time spent manually searching recorded footage.
Provides event timestamps and visual evidence for verification.
Can be adapted to different retail environments.
Demonstrates practical applications of AI, computer vision, and activity recognition.

No review given yet!

Fast Delivery all across the country

Safe Payment

7 Days Return Policy

100% Authentic Products

Shopping cart