Computer Vision System

Project Overview

This computer vision system was developed to solve inventory management challenges in retail environments. By leveraging deep learning and object detection algorithms, the system can accurately track products on shelves, identify misplaced items, and detect stockouts in real-time.

The system uses a network of strategically placed cameras throughout a store to continuously monitor inventory levels. Advanced object detection models identify products with high accuracy, even in varying lighting conditions and when partially obscured.

Technical Approach

The project was implemented using a combination of state-of-the-art computer vision techniques and deep learning models:

• Object Detection: Utilized a custom-trained YOLOv5 model to detect and classify products
• Tracking: Implemented DeepSORT algorithm for consistent object tracking across video frames
• Data Pipeline: Built scalable data processing pipeline using Kafka and Spark for real-time video analysis
• Model Optimization: Applied TensorRT for model optimization and faster inference times on NVIDIA GPUs
• Edge Deployment: Deployed optimized models on edge devices (like NVIDIA Jetson) for distributed processing

Key Challenges

Developing this system presented several significant challenges:

• Varying Lighting Conditions: Retail environments have inconsistent lighting that affects detection accuracy. Addressed using data augmentation (brightness, contrast changes) and robust model training.
• Product Similarity: Many products have similar packaging that makes classification difficult. Solved by fine-tuning the model on highly specific product datasets and using higher resolution inputs.
• Occlusion: Products often overlap or are partially hidden from view. Mitigated by training the model to recognize partially visible objects and using tracking algorithms to maintain identity.
• Real-time Processing: The system needed to operate in real-time with minimal latency. Achieved through model optimization (TensorRT) and edge deployment.
• Scale: Solution needed to scale across multiple cameras and store locations. Handled via a distributed architecture and efficient data streaming (Kafka).

Results & Impact

The implemented system has significantly improved inventory management efficiency, reducing manual audit time by over 70% and enabling staff to focus on customer service rather than inventory checks. The real-time nature of the system allows for immediate corrective actions, leading to better product availability and customer satisfaction.

Additionally, the data collected by the system provides valuable insights into customer shopping patterns and product interaction (e.g., dwell time heatmaps), which has informed better store layout decisions and promotional strategies.