Tools and Technologies Used in the Project
- Numpy: The fundamental package for scientific computing with Python.
- OpenCV: “ Open Source Computer Vision Library ” is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications.
- ImageAI: It is a Python library built to empower developers, researchers, and students to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. ImageAI provides the three most powerful models for object detection and tracking – RetinaNet, YOLOv3, and TinyYOLOv3.
- In our project, we have used YOLOv3 as it gives a moderate performance with accuracy and moderate detection speed and time.
- Streamlit: It is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.
Project Idea – Object Detection and Tracking
Project title: Object Detection and Tracking
Introduction: A lot of people go to supermarkets and retail stores and shops to idle around and window-shop instead of purchasing any products. The thought of analyzing this kind of behavior was intriguing.
- How does this kind of behavior affect product sales?
- What time periods these people were coming in?
- What could help the owners count the number of customers by cross-referencing the billing data, but how do you count the people who haven’t shopped?
Object detection and tracking is one of the areas of computer vision that is maturing very rapidly. It allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection and tracking can be used to count objects in a particular scene and determine and track their precise locations, all while accurately labeling them.
In this project, we have made use of two of the most popular Python libraries for object detection, OpenCV and ImageAI.
Every supermarket nowadays has at least one CCTV camera installed. And the data is stored in a centralized repository with a timestamp. Our end goal was to identify the people coming in and going out of the supermarket or retail store, and categorize them under the labels “customer” or “not a customer”. By achieving this goal we could calculate the actual cost per customer.