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Moving Object Detection and Tracking by Event Frame from Neuromorphic Vision Sensors.

Jiang Zhao1, Shilong Ji1, Zhihao Cai1

  • 1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Biomimetics (Basel, Switzerland)
|March 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for moving object detection and tracking using event cameras, enhancing accuracy in challenging visual conditions. The approach leverages event frames for improved object detection, tracking, and distance estimation.

Keywords:
event frameneuromorphic vision sensorsobject detectionobject tracking

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Area of Science:

  • Computer Vision
  • Robotics
  • Neuromorphic Engineering

Background:

  • Standard cameras struggle with fast motion and illumination changes, impacting object detection and tracking accuracy.
  • Event cameras offer a low-latency, high-dynamic-range alternative by capturing scene changes asynchronously.
  • Existing methods often face challenges with missed detections and inaccurate distance measurements in dynamic environments.

Purpose of the Study:

  • To develop an improved system for moving object detection and tracking using bio-inspired event cameras.
  • To enhance the accuracy of object detection by combining event frames with standard camera frames.
  • To refine object tracking and distance estimation using event-based data and advanced algorithms.

Main Methods:

  • A hybrid object detection approach combining event frames (probability-based) and standard frames (color-based).
  • A detection-based object tracking method utilizing event frames and an improved kernel correlation filter to minimize missed detections.
  • A distance measurement technique employing event frame-based tracking and similar triangle theory for precise object-to-camera distance estimation.

Main Results:

  • The proposed methods demonstrate significant effectiveness in moving object detection and tracking tasks.
  • The combined frame approach improved detection reliability under adverse conditions.
  • Enhanced accuracy in tracking and distance estimation was achieved using the event-based system.

Conclusions:

  • Event cameras provide a robust solution for object detection and tracking, overcoming limitations of traditional cameras.
  • The developed hybrid detection and event-based tracking system offers superior performance in dynamic visual scenes.
  • This research contributes to advancements in real-time computer vision applications requiring reliable motion analysis.