Create a Smart Retail Analytics Solution

Use computer vision inference in the Intel® Distribution of OpenVINO™ toolkit to provide analytics on customer engagement, store traffic, and shelf inventory.

Target Operating System Ubuntu* 16.04 LTS
Time to Complete 60 minutes

GitHub* (Python*)

What You Will Learn

Create multiple video input streams and a computer vision inferencing pipeline to optimize retail operations.

Gain insight into the following solutions:

  • Computer vision applications for IoT
  • Inference to analyze video data
  • Retail market IoT

Use the skills learned in this reference implementation to develop similar IoT solutions.

Learn to build and run an application with these capabilities:

Detect people within a designated area by displaying a green bounding box over them, count the total number of people, and the time they are in the frame.
Use an inferencing pipeline to detect faces, emotions, and head poses.
Visualize the output in a data-rich dashboard.

How It Works

The application is split into three containerized components: one responsible for store traffic monitoring through people detection, one for detecting a shopper's gaze and sentiment, and one for checking the shelf inventory.

  1. A trained neural network detects people within a designated area by using the mobilenet-ssd model for inferencing, and then stores and displays the current number of people in the frame and the historical total.
  2. An inferencing pipeline uses a face detection model to identify the presence of a shopper looking at a product. This data is passed to an emotion recognition model and head-pose detection model to determine engagement and sentiment.
  3. The captured analytics data is sent to an InfluxDB for storage and further analysis, with a data-rich dashboard for visualization using Grafana.