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A Protocol for Real-time 3D Single Particle Tracking
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Intelligent Trigonometric Particle Filter for visual tracking.

Hathiram Nenavath1, K Ashwini1, Ravi Kumar Jatoth2

  • 1Department of Electronics and Communication Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, 501218, India.

ISA Transactions
|October 6, 2021
PubMed
Summary
This summary is machine-generated.

A new Trigonometric Particle Filter (TPF) improves visual tracking accuracy by optimizing particle sets with the Sine Cosine Algorithm (SCA). This method overcomes particle impoverishment in traditional Particle Filters (PF), enhancing surveillance system performance.

Keywords:
OcclusionParticle filter (PF)Sine Cosine Algorithm (SCA)Trigonometric Particle Filter (TPF)Visual tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Particle Filter (PF) is crucial for visual tracking in intelligent surveillance.
  • PF's re-sampling process can lead to sample impoverishment, reducing tracking precision.

Purpose of the Study:

  • Propose a novel Trigonometric Particle Filter (TPF) to enhance visual tracking accuracy.
  • Address the sample impoverishment issue in Particle Filters.

Main Methods:

  • Developed TPF by optimizing PF with the Sine Cosine Algorithm (SCA).
  • Integrated SCA before the re-sampling step to enrich the particle set.
  • Evaluated TPF performance on Visual Tracker Benchmark (VOT) databases.

Main Results:

  • TPF demonstrated more consistent and proficient tracking outcomes.
  • Outperformed evolutionary-based PFs (SMO-PF, FAPF, PSO-PF), correlation filter trackers, and other state-of-the-art methods.

Conclusions:

  • The proposed TPF effectively enhances visual tracking precision.
  • TPF offers a superior alternative to existing methods for intelligent surveillance applications.