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This study introduces a novel vehicle tracking method using contour detection for aerial videos. The approach enhances tracking accuracy by adapting to target shapes, outperforming existing state-of-the-art methods.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Aerial videos present challenges for vehicle tracking due to small object sizes.
  • Current tracking methods often rely on fixed bounding boxes, limiting accuracy.
  • Object contour detection offers a more informative and background-minimal representation for tracking.

Purpose of the Study:

  • To improve the accuracy of vehicle locating and tracking in aerial videos.
  • To develop a shape-adaptive tracking method utilizing object contours.
  • To enable real-time tracking and guidance systems.

Main Methods:

  • A novel tracking system initiated by clicking on the target.
  • Modification and combination of a contour detection network and a fully convolutional Siamese tracking network.
  • Utilizing shape-adaptive templates segmented by object contours for improved information extraction.

Main Results:

  • Significantly improved tracking accuracy compared to state-of-the-art methods.
  • Demonstrated effectiveness on standard datasets (OTB100 and DARPA).
  • Validated performance on vehicle images in aerial video sequences.

Conclusions:

  • The proposed contour-based tracking method enhances accuracy for small vehicles in aerial footage.
  • The system is suitable for real-time applications in tracking and guidance.
  • Contour information provides a robust basis for shape-adaptive object tracking.