Company

Camera-Based Driver Activity Recognition for Driver Monitoring System

Our client is a $50 billion public corporation that provides cutting-edge technology solutions across the globe. One of their fastest-growing divisions is the automotive solutions division that aims at creating new standards for vehicles with the vision of improving driving safety through Advanced Driver Assistance Systems (ADAS). The company approached us in 2019 with the request to develop a Computer Vision-powered Driver Monitoring System (DMS) based on driver activity recognition.

 

Solution

The project entrusted to Abto Software included three key tasks:

  • to analyze state-of-the-art Deep Learning and Computer Vision approaches to vision-based activity recognition;
  • to determine optimal in-cabin camera placement for the task at hand;
  • to develop a Driver Monitoring System capable of real-time driver activity recognition and its temporal localization with 90%+ accuracy.

The major goal of the project was defined as ensuring the driver’s focus on the road and increasing road safety for all traffic participants.

 

Due to the complex nature of the project, we split its execution into six phases:

  • Planning
  • Development of the video capturing and annotation tool
  • Data collection
  • Analysis of activity recognition approaches
  • Development of the AI-driven driver activity recognition model
  • Development of the toolkit for adding recognized activities

 

Team: project manager/subject-matter expert, 3 Deep Learning specialists, R&D Computer Vision specialist, 2 software developers, QA, data annotator.

 

Tech stack: TensorFlow, Two-stream CNN, Inception v4, OpenCV, optical flow.

 

Results

Abto Software has delivered a Driver Monitoring System capable of performing real-time driver’s activity type classification and its temporal localization with 90%+ accuracy. The software application recognizes 6 types of driver’s actions from a single video source and supports two possible camera positions in the vehicle cabin.

In combination with driver’s fatigue detection and driver’s health tracking technologies the developed Driver Monitoring System enables comprehensive identification of in-cabin situations and ensures safe driving in automated and semi-automated vehicles.

Release Date
2019-05-01
Duration
3 months
Customer
$50 billion public corporation

Industries
Automotive
Services
Artificial Intelligence | Software Quality Assurance | Project Management | Data Annotation
Technologies
C++ / Windows | C++ / Linux