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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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MEVDT: Multi-modal event-based vehicle detection and tracking dataset.

Zaid A El Shair1, Samir A Rawashdeh1

  • 1Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, 48128 MI, USA.

Data in Brief
|January 13, 2025
PubMed
Summary

A new dataset, Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT), offers synchronized event and image data for advanced vehicle detection and tracking research. This resource aids in developing and evaluating algorithms for automotive environments.

Keywords:
Computer visionEvent-based visionMultimodalObject detectionObject tracking

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Area of Science:

  • Computer Vision
  • Robotics
  • Automotive Engineering

Background:

  • Event-based cameras offer advantages in high-dynamic-range and high-speed scenarios.
  • Existing datasets lack synchronized multi-modal data for robust vehicle detection and tracking.
  • Developing advanced algorithms requires high-quality, real-world annotated datasets.

Purpose of the Study:

  • Introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset.
  • Provide a synchronized stream of event data and grayscale images for traffic scenes.
  • Facilitate research in event-based vision for automotive applications.

Main Methods:

  • Utilized a Dynamic and Active-Pixel Vision Sensor (DAVIS) 240c camera.
  • Captured 63 multi-modal sequences comprising images and event data.
  • Manually annotated ground truth labels at 24 Hz, including object classifications, bounding boxes, and IDs.

Main Results:

  • The MEVDT dataset contains approximately 13k images, 5M events, and 10k object labels.
  • Includes 85 unique object tracking trajectories with pixel-precise annotations.
  • Offers a comprehensive resource for training and evaluating object detection and tracking algorithms.

Conclusions:

  • MEVDT addresses the need for high-quality, real-world annotated datasets in event-based vision.
  • The dataset enables advancements in vehicle detection and tracking algorithms for autonomous driving.
  • Facilitates the development of more robust and efficient perception systems for automotive environments.