Action Recognition based Industrial Safety Violation Detection
This addresses safety monitoring for industry workers by reducing false alarms, though it is incremental as it combines existing activity recognition and object detection methods.
The paper tackles the problem of high false alarm rates in industrial safety violation detection by proposing a system that first recognizes worker actions and then customizes PPE requirement checks, achieving a 23% improvement in F1-score on a dataset of 109 videos.
Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.