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High-Temporal-Resolution Object Detection and Tracking Using Images and Events.

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

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Summary

This study introduces a hybrid approach for object detection and tracking using both traditional frame-based and novel event-based vision. The method achieves robust, high-temporal-resolution tracking, even at increased frame rates.

Keywords:
event-based visionframe-based visionhigh-temporal-resolution trackinghybrid approachobject detection and tracking

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Event-based vision sensors offer asynchronous data streams and high temporal resolution, complementing traditional frame-based cameras.
  • Combining frame-based and event-based vision can enhance object detection and tracking capabilities, especially in dynamic environments.

Purpose of the Study:

  • To develop and evaluate a hybrid object detection and tracking system integrating frame-based and event-based vision data.
  • To assess the performance of the hybrid system at various high temporal resolutions.

Main Methods:

  • Utilized off-the-shelf frame-based detectors for initial object detection and classification.
  • Employed event masks from synchronized event data for inter-frame tracking at varying temporal resolutions.
  • Developed a novel traffic dataset with ground truth bounding boxes and object IDs for quantitative evaluation.

Main Results:

  • The hybrid approach demonstrated robust object detection and tracking performance.
  • Tracking accuracy showed minimal degradation even at high temporal resolutions (48-384 Hz) compared to a 24 Hz baseline.
  • A new, labeled traffic dataset was created for comprehensive performance analysis.

Conclusions:

  • The proposed hybrid system effectively leverages the strengths of both frame-based and event-based vision for high-temporal-resolution object tracking.
  • The approach maintains performance across a wide range of temporal resolutions, offering flexibility for various applications.
  • The developed dataset facilitates further research and benchmarking in event-based vision and sensor fusion for object tracking.