Speech Data Collection for In-Cabin Recognition

The automotive industry is undergoing a major transformation, driven by technological advancements and the growing demand for intelligent and safer vehicles. Two key technologies that are playing a pivotal role in this transformation are OMS (Occupant Monitoring System) and DMS (Driver Monitoring System). These systems, coupled with behavior recognition capabilities, are revolutionizing the automotive landscape.

OMS, also known as OCS (Occupant Classification System), is designed to monitor and analyze the behavior and characteristics of vehicle occupants. It utilizes advanced sensors and cameras to detect and classify occupants, ensuring their safety and optimizing comfort. OMS can detect the presence of occupants, their seating positions, and even determine whether they are wearing seat belts. By accurately identifying occupants, OMS enables intelligent airbag deployment, personalized climate control, and targeted safety notifications.

On the other hand, DMS focuses on monitoring and analyzing the behavior of the driver. Using a combination of cameras, infrared sensors, and artificial intelligence algorithms, DMS tracks the driver's eye movement, head position, and other facial features. This information is crucial for detecting driver drowsiness, distraction, and fatigue, which are common causes of accidents. DMS can provide real-time alerts to the driver, suggesting breaks or raising alarms in critical situations.

However, the real game-changer is the integration of behavior recognition capabilities with OMS and DMS. By analyzing the behavior patterns of both occupants and drivers, automotive manufacturers can gain valuable insights into their customers' preferences, comfort levels, and safety requirements. This data can be used to enhance the overall driving experience, tailor vehicle features, and improve product development.

Behavior recognition in the automotive sector is powered by cutting-edge artificial intelligence and machine learning algorithms. These algorithms can analyze vast amounts of data collected from sensors and cameras in real-time, identifying patterns, and predicting future behavior. For example, they can anticipate a driver's intention to change lanes by analyzing their eye gaze and head movement. This enables proactive safety measures, such as blind spot detection and lane departure warning systems.

The impact of OMS, DMS, and behavior recognition on the automotive industry cannot be overstated. With these technologies, vehicles are becoming safer, more intuitive, and personalized to the needs of the occupants. The integration of behavior recognition also paves the way for advancements in autonomous driving. By continuously monitoring driver and occupant behavior, autonomous vehicles can adapt to their preferences, making the journey more comfortable and enjoyable.

Nexdata OMS and DMS Data Solutions

103,282-Images Driver Behavior Annotation Data

103,282-Images Driver Behavior Annotation Data. The data includes multiple ages, multiple time periods and behaviors (Dangerous behaviors, Fatigue behaviors, Visual movement behaviors). In terms of annotation, 72 facial landmarks (including pupils), face attributes, gesture bounding boxes, seatbelt bounding boxes, pupil landmarks and behavior categories were annotated in the data. This data can be used for tasks such as driver behavior analysis.

122 People - Passenger Behavior Recognition Data

122 People - Passenger Behavior Recognition Data. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as passenger behavior analysis.

1,003 People-Driver Behavior Collection Data

1,003 People-Driver Behavior Collection Data. The data includes multiple ages and multiple time periods. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.