Build a Safety Gear Detector

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.

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.