Build a Safety Gear Detector
Observe workers as they pass in front of a camera and determine if they have adequate safety protection.
Target Operating System: Ubuntu* 16.04 LTS
Time to Complete: 45 minutes
Software Used:
Intel® Distribution of OpenVINO™ Toolkit
Models Used:
What You Will Learn
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.
What You Need
Hardware Requirements
Software Requirements
Ubuntu 16.04 LTS (preinstalled on the hardware)
OpenCL™ Runtime package
Models Used