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
Software Used:
Intel® Distribution of OpenVINO™ Toolkit
Models Used:
head-pose-estimation-adas-0001
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
Learn to build and run an application with these capabilities:
- ❶ 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.
- ❷ Use an inferencing pipeline to detect faces, emotions, and head poses.
- ❸ 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.
❶ 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.
❷ 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.
❸ The captured analytics data is sent to an InfluxDB for storage and further analysis, with a data-rich dashboard for visualization using Grafana.
What You Need
Hardware Requirements
Software Requirements
Ubuntu 16.04 LTS (preinstalled on the hardware)
OpenCL™ Runtime package
Models Used
head-pose-estimation-adas-0001