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An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection.

Qing Li1, Shaopeng Hu1, Kohei Shimasaki1

  • 1Smart Robotics Laboratory, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Hiroshima, Japan.

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Summary
This summary is machine-generated.

This study introduces a visual tracking system capable of simultaneously detecting and tracking multiple fast-moving objects at 500 frames per second. The system utilizes a CNN-based hybrid algorithm for robust performance in complex scenarios.

Keywords:
convolutional neural network (CNN)high-speed visionmulti-object trackingtemplate matching (TM)

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Area of Science:

  • Computer Vision
  • Robotics
  • Image Processing

Background:

  • Tracking multiple fast-moving objects presents challenges due to appearance variations and high speeds.
  • Existing systems often struggle with simultaneous tracking of numerous dynamic targets.

Purpose of the Study:

  • To develop a high-speed visual tracking system for simultaneous detection and tracking of multiple appearance-varying targets.
  • To achieve robust tracking performance at 500 frames per second.

Main Methods:

  • Utilized a high-speed camera and a pan-tilt galvanometer system for rapid image acquisition.
  • Developed a Convolutional Neural Network (CNN)-based hybrid tracking algorithm.
  • Implemented simultaneous zoom shooting for multiple moving objects (persons, bottles) in outdoor scenes.

Main Results:

  • Successfully tracked up to three moving objects simultaneously within an 8-meter range.
  • Achieved tracking of objects with velocities up to 30 meters per second.
  • Demonstrated high robustness to target loss and crossing situations.

Conclusions:

  • The proposed visual tracking system effectively handles multiple, fast-moving, appearance-varying targets.
  • The CNN-based hybrid algorithm provides robust and reliable multi-object tracking.
  • The system shows significant potential for applications requiring high-speed, multi-target surveillance and analysis.