Shoplifting Detection in Retail Stores Using Computer Vision and Machine Learning

Local Chapter Nairobi, Kenya Chapter

Coordinated byKenya ,

Status: Completed

Project Duration: 07 Aug 2023 - 09 Sep 2023

Open Source resources available from this project

Project background.

Shoplifting is a significant issue faced by retailers worldwide, including Kenya. It results in financial losses, increased prices for consumers, and decreased profitability for businesses. Traditional shoplifting prevention methods are often ineffective, and there is a need for innovative solutions to tackle this problem.

The problem.

Shoplifting is a prevalent problem in Kenya, leading to substantial losses for retailers. Current surveillance systems in stores rely on manual monitoring, which is time-consuming, costly, and prone to human error. There is a need to develop an automated shoplifting detection system that can accurately identify and alert store staff about potential shoplifting incidents in real time. This system would help prevent theft, reduce losses, and enhance the overall security of retail stores.

Project goals.

The project goals are:- Develop a shoplifting detection system using computer vision and machine learning techniques. - Create an automated alert system that notifies store staff in real time when a potential shoplifting incident is detected. - Improve security and reduce financial losses for retail stores in Kenya. - Enhance the overall shopping experience for customers by providing a safe and secure environment.

Project plan.

  • Week 1

    Conduct research on existing shoplifting detection systems and technologies.
    Define the scope and objectives of the project.
    Set up the development environment and tools.
    Collect, annotate and preprocess a dataset of surveillance videos.

  • Week 2

    Explore computer vision algorithms for object detection.
    Implement and train a baseline model for detecting people in surveillance videos.

  • Week 3

    Investigate and implement additional object detection algorithms suitable for shoplifting detection. Fine-tune the models using the collected dataset.Develop a real-time monitoring system that processes surveillance videos and identifies potential shoplifting incidents. Implement an alert mechanism to notify store staff when suspicious activities are detected

  • Week 4

    .Evaluate the performance of the developed system using a separate test dataset. Fine-tune the models and optimize the system for better accuracy and speed. Conduct extensive testing and validation of the system. Document the project, including the technical implementation, challenges faced, and lessons learned. Prepare a final report and presentation summarizing the project outcomes and achievements.

Learning outcomes.

– Gain hands-on experience in computer vision and object detection algorithms.
– Learn about machine learning techniques for anomaly detection.
– Understand the challenges and considerations in developing real-time surveillance systems.
– Develop skills in data preprocessing, model training, and evaluation.
– Collaborate with a diverse team of professionals in a remote setting.
– Enhance problem-solving and critical-thinking skills.

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