EMEA Hackathon
in SAS® Viya® 2020

Hackathon in SAS® Viya®


Topic of the Challenge:

Promote and enhance workplace safety with SAS Viya

On average, each of us spends approximately 30% of their time at workplace. According to survey conducted by the Society for Human Resource Management, feeling safe at work was ranked third among the top five priorities of job satisfaction. Therefore, as it is stated in the Occupational Safety and Health Act, keeping employees safe and secure is the employer’s most important responsibility.

Workplace safety encompasses many aspects, but we decided to focus on the protective safety equipment and preventing workplace injuries. Both the lack of protective safety equipment and unreported workplace violation can affect employees’ productivity and efficiency. Moreover, in the world of massive libraries of video materials, we cannot rely on human performed analysis anymore and produce agile and rapid decisions.

Therefore, our goal is to develop and deploy the solution which will use the computer vision analyzing the endless stream of video data in real-time and executing disparate pre-defined decision rules, for example:

  • Should the door be opened to the outsider who has someone’s identification badge or an electronic key;
  • Check if all the employees on the field are using safety helmets, and alert immediately if not.

We will combine effectively two different aspects: the image recognition algorithms (taken as a base) and event streaming processing tools, hence creating a model that can meet any kind of employee’s safety requirements. As a result, only approved workers will be able to access the restricted company premises, no forgotten safety helmets will cause injuries at workplace and overall, critical accidents happening due to violation of safety rules will be minimized. 

Click on the Play button below and watch Accentures´s contest contribution!

Team Name:

Greater than

Diana Rimsane
Team Lead

Darja Jafanova
Team Member

Ekaterina Dmitrieva
Team Member

Olena Negolsha
Data engineer