Advanced Theft detection methods in Retail Industry

Project Overview: Theft Detection System

Our project is centered on developing a robust theft detection system aimed at enhancing security in retail environments by using state-of-the-art video analysis technology. Prompted by the escalating incidence of shoplifting and other theft-related activities that not only result in financial losses but also undermine the shopping experience, we committed to creating a safer space for both shoppers and retail owners.

Motivation

The initiative to develop this project was driven by a desire to leverage technology in combating the rising tide of retail crimes. By integrating advanced machine learning techniques, we aimed to offer a proactive tool that could detect potential theft incidents before they escalate, thereby securing assets and ensuring a secure shopping environment.

Collaborations and Research

In the initial phase, we approached several major retailers, including Reliance, More, Hypermarkets, Spar, and Dmart, to understand their specific security challenges and the limitations of existing surveillance systems. This engagement provided us with valuable insights into the practical aspects of theft prevention and the operational intricacies of retail security management.

Data Collection and Preparation

To train our model, we created an in-house dataset consisting of 93 normal behavior videos and 95 suspicious behavior videos, meticulously curated to define and differentiate pre-crime behaviors from normal activities. This dataset was essential in training our system to recognize subtle cues that precede a theft, allowing for timely interventions.

Technical Approach

Our solution employs a Long-term Recurrent Convolutional Network (LRCN) model, which combines Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to analyze video data. This model architecture is particularly adept at understanding both spatial and temporal patterns, making it ideal for detecting activities over time in video sequences.

Model Development Steps:

• Data Visualization: Initially, we visualized the video data along with its labels to understand the types of behaviors captured in our dataset.
• Preprocessing: We processed the videos to standardize their format, resizing frames and normalizing pixel values to facilitate efficient learning by the model.
• Model Training: We split the data into training and test sets, with a training strategy that included validation checks to prevent overfitting and ensure the model's generalizability.

• Performance Evaluation: After training, the model's effectiveness was assessed through various metrics, including accuracy and loss curves, to measure its predictive capabilities in real-world scenarios.

• Deployment Testing: Finally, we tested the best-performing model on new video data to verify its real-time applicability and effectiveness in detecting suspicious behaviors.

Conclusion and Future Work

The project has shown promising results in identifying potential theft scenarios with high accuracy. Moving forward, we aim to refine the model's sensitivity to diverse and subtle theft behaviors and explore integration with existing CCTV systems for seamless deployment in retail environments. Our goal is to create a universally applicable solution that enhances retail security globally, adapting continuously to new challenges in theft prevention.