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.

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.