Driver Monitoring System

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Uses computer vision and machine learning algorithms to monitor the driver's behaviour, attentiveness, and overall condition while driving.

In-cabin sensing using computer vision refers to the application of computer vision techniques to monitor and analyze the interior of a vehicle's cabin. By using cameras and computer vision algorithms, in-cabin sensing systems can detect and interpret various information about the occupants and their activities inside the vehicle.

In-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI).

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In Cabin Monitor System [IMS]

In-cabin sensing using computer vision refers to the application of computer vision techniques to monitor and analyze the interior of a vehicle's cabin. By using cameras and computer vision algorithms, in-cabin sensing systems can detect and interpret various information about the occupants and their activities inside the vehicle.

In-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI).

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Monitoring and surveillance are essential in highly automated driving. Broader categorizations are related to the safety, security, and privacy of the occupants.

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Optimize Your Monitoring With Nebula DMS

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Gaze Detection

Check if the driver is focused on the road, mirrors, or other important visual cues. It can also help detect driver distraction if the gaze is consistently directed away from the driving environment.

  • Detect Driver's distraction
  • Blink detection to identify tiredness
  • Identify micro-sleep
  • Alert the Driver to avoid possible crashes

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Facial Recognition

To capture the driver's face and track facial features in real time. Facial recognition algorithms analyze facial landmarks such as eyes, eyebrows, mouth, and head position to understand the driver's state.

  • Secure login
  • Emotion detection
  • Detect driver attention level
  • Enhanced cyber security

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Heart-Rate Monitoring

Detect the heart-rate of the driver which can help detect driver fatigue, stress, or health issues.

  • Detect subtle color changes in the faces caused by blood flow to measure heart rate.
  • Provides the heart beats per minute (BPM) and the heart rate diagram measured from the changes in face color.
  • Trigger appropriate alerts or interventions to ensure driver safety.

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Fatigue Detection

Detects signs of driver fatigue, distraction, or impairment

  • Warns the driver of drowsiness and the risk of a microsleep
  • Compliance with driver warnings helps to avoid crashes caused by fatigue

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Age and Gender Detection

Detect users age and gender

  • Automate the driver experience according to age & gender preference
  • To monitor the in-car occupancy activity
  • Provide the safety measures by distinguishing the child and adult passengers