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Adaptive ensemble perception tracking.

Zikun Zhou1, Nana Fan1, Kai Yang1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive ensemble perception tracking framework. It enhances object tracking by improving target perception at both object and neuron levels, achieving better accuracy and speed.

Keywords:
Ensemble predictionReceptive field adaptionSiamese networkVisual tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Siamese network-based trackers with bounding box regression are popular but struggle with large-scale targets and scale variations.
  • Existing models have limited receptive fields, hindering global target perception and adaptability.

Purpose of the Study:

  • To propose an adaptive ensemble perception tracking framework to overcome limitations in current object tracking models.
  • To enhance target perception by addressing issues with large-scale targets and scale/aspect ratio changes.

Main Methods:

  • Developed a per-pixel prediction model for target state estimation at each feature pixel.
  • Implemented a confidence-guided ensemble mechanism to fuse predictions adaptively, enlarging perception range.
  • Introduced a receptive field adaptation model to adjust neuron-level receptive fields for better adaptability.

Main Results:

  • The proposed framework demonstrates improved perception range and adaptive abilities at both object and neuron levels.
  • Achieved favorable performance against state-of-the-art methods on benchmark datasets (VOT2018, VOT2016, UAV123, LaSOT, TC128).

Conclusions:

  • The adaptive ensemble perception tracking framework effectively enhances object tracking accuracy and speed.
  • The method successfully addresses challenges posed by large-scale targets and dynamic target appearance changes.