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An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform.

Zihao Wang1,2, Sen Yang1,2, Mengji Shi1,2

  • 1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an image augmentation model to enhance object tracking on mobile devices, improving detection accuracy by over 10% even with limited samples and camera movement. The method boosts generalization for more reliable object recognition.

Keywords:
blur augmentationlimited samplesmonocular visionprojection augmentationreal-time object tracking

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Object tracking on mobile platforms faces challenges due to limited effective samples, platform jitter, and relative camera-object rotation.
  • Detection failures in object tracking systems often stem from insufficient training data and variations in environmental conditions.

Purpose of the Study:

  • To develop an effective image augmentation model for object tracking on mobile devices, addressing limitations of small sample sizes and tracking instability.
  • To enhance the generalization ability and robustness of object detection models used in tracking applications.

Main Methods:

  • Proposed an image augmentation model incorporating geometric projection transformation, multi-directional overlay blurring, and random background filling.
  • Integrated traditional augmentation techniques with adjustable probability factors to simulate diverse sample scenarios for robust model training.
  • Combined the augmentation module with a spatial localization algorithm based on geometric constraints for a comprehensive object tracking framework.

Main Results:

  • The proposed augmentation module improved detection accuracy by at least 10% for various object detection models (SSD, YOLOv3, YOLOv4, YOLOx).
  • Affine and projection transformations significantly enhanced detection accuracy for objects with planar characteristics.
  • The object tracking framework achieved a Root Mean Square Error (RMSE) of less than 4.21 cm for indoor object tracking.

Conclusions:

  • The developed image augmentation model effectively addresses key challenges in mobile object tracking, leading to substantial improvements in detection accuracy and tracking precision.
  • The framework provides a robust solution for real-time object tracking applications on mobile platforms, demonstrating significant performance gains.
  • The augmentation strategy enhances model generalization, making it more resilient to variations in sample size and tracking conditions.