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Human Segmentation and Tracking Survey on Masks for MADS Dataset.

Van-Hung Le1, Rafal Scherer2

  • 1Department of Information Technology, Tan Trao University, Tuyen Quang 22000, Vietnam.

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Summary

This study surveys human segmentation and tracking methods using Convolutional Neural Networks (CNNs). It introduces the MASK MADS dataset for evaluating these techniques on complex activities.

Keywords:
MADS datasetconvolutional neural networkshuman segmentationhuman tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human segmentation and tracking heavily rely on accurate person detection in videos.
  • Convolutional Neural Networks (CNNs) have shown significant advancements in these areas.
  • Applications include video monitoring and human pose estimation in 2D and 3D.

Purpose of the Study:

  • To conduct a comprehensive survey of human segmentation and tracking methods in videos.
  • To analyze the impact of person detection on segmentation and tracking performance.
  • To introduce a new mask dataset for evaluating these tasks.

Main Methods:

  • Survey of existing literature, methods, datasets, and results for human segmentation and tracking.
  • Detailed examination of Convolutional Neural Networks (CNNs) approaches.
  • Creation and utilization of the MASK MADS dataset for evaluation.

Main Results:

  • The survey provides an in-depth review, including source code paths.
  • The MASK MADS dataset contains 28,000 mask images for segmentation and tracking evaluation.
  • Recent CNNs methods were evaluated on the MADS dataset for segmentation and tracking.

Conclusions:

  • Accurate person detection is crucial for effective human segmentation and tracking.
  • The MASK MADS dataset offers a valuable resource for benchmarking segmentation and tracking algorithms.
  • The study highlights the effectiveness of CNNs in complex human activity analysis.