Using a video camera as part of a digital kiosk system, this application identifies the age and gender of the audience standing in front of a digital sign. Based on the identification, the application selects a suitable 4K advertisement. Real-time data visualization occurs on Grafana*, which enables developers to monitor trends over time.
Gain insight into the following solutions:
Computer vision applications for IoT
Inference to analyze datasets
Retail market IoT
Learn to build and run an application with these capabilities:
❶ Determine age, gender, and head pose with deep neural network (DNN) models.
❷ Play a 4K ad based on audience identification.
❸ Visualize analytics using a combination of InfluxDB* and Grafana.
How It Works
Using a combination of different computer vision techniques, this application detects whether workers within a video frame are wearing safety gear, and if not, applies a red bounding box over the image of the worker. The video displays the total number of workers without safety gear.
❶ The inference engine in the Intel Distribution of OpenVINO toolkit uses a trained neural network to process the video and detect people in the video frame. If detected, the image of the person is cropped.
❷ Within the cropped image, traditional computer vision techniques detect the presence of specific colors, namely the yellow of a hard hat and the orange of a safety vest.
If appropriate wear is recognized, a green bounding box highlights the worker.
If safety gear is missing, a red bounding box highlights the worker and the application counts them.
❸ The final image frame displays workers not wearing safety gear and the overall statistics in the bottom left corner.