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A Protocol for Real-time 3D Single Particle Tracking
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Real-Time Multiobject Tracking Based on Multiway Concurrency.

Xuan Gong1,2, Zichun Le2, Yukun Wu1,3

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a new framework for multiway concurrent multiobject tracking (MOT) systems, enhancing real-time performance for parallel video streams. The approach optimizes resource usage and tracking accuracy for practical applications.

Keywords:
concurrencymultiobject trackingmultiwayreal-timesingle-object trackingtracking-by-detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Real-time Systems

Background:

  • Current multiobject tracking (MOT) research primarily focuses on single-image sequences.
  • Practical applications increasingly require MOT systems to process multiple concurrent video streams in parallel.
  • Limited studies address the real-time performance challenges of multiway concurrent MOT systems.

Purpose of the Study:

  • To propose a novel MOT framework designed for multiway concurrency and real-time performance.
  • To address limitations in computing and storage resources while maintaining algorithm efficiency.
  • To investigate factors influencing system real-time performance and concurrency in practical scenarios.

Main Methods:

  • Developed a tracking-by-detection (TBD) based framework with expanded width and depth for parallel video processing.
  • Introduced image and bounding box collectors for batch processing capabilities.
  • Proposed a real-time MOT algorithm based on directly driven detection and optimized deep neural networks (DNNs) using NVIDIA TensorRT.
  • Implemented a negative sample filter to ensure tracking accuracy and balance performance.

Main Results:

  • The proposed framework effectively supports simultaneous processing of multiway video sequences.
  • The system demonstrates good performance in handling multiple concurrent real-time video streams.
  • Optimization techniques significantly accelerated DNN inference within the tracking algorithm.

Conclusions:

  • The developed MOT framework offers a pragmatic solution for real-time multiway concurrency.
  • The approach balances tracking accuracy with the demands of real-time processing on limited resources.
  • This work advances the capabilities of MOT systems for complex, real-world video surveillance and analysis.