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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review.

Dengshan Li1,2,3, Rujing Wang1,3, Peng Chen4

  • 1Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

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Summary

This review discusses video object and human action detection methods, focusing on supervised learning approaches. It covers classic techniques, datasets, and classifications like frame-based and temporal information utilization for enhanced video analysis.

Keywords:
LSTMdeep learninghuman action recognitionoptical flowtemporal informationvideo datasetvideo object detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Video object detection and human action recognition are crucial for applications like surveillance and face recognition.
  • Video detection presents unique challenges including blur, defocus, motion blur, and occlusion, making it more complex than image detection.
  • Despite challenges, current video detection technology achieves real-time and high-accuracy performance, even on blurry frames.

Purpose of the Study:

  • To review and discuss various video object and human action detection approaches.
  • To focus on classic video detection methods employing supervised learning.
  • To examine commonly used datasets for video object detection and human action recognition.

Main Methods:

  • Review of supervised learning-based video detection techniques.
  • Analysis of methods for object classification and location within video frames.
  • Discussion of human action recognition approaches.

Main Results:

  • Many reviewed methods achieve state-of-the-art results in video detection and action recognition.
  • Video detection methods are classified into frame-by-frame, key-frame extraction, and temporal information utilization.
  • Temporal information is often leveraged using optical flow, Long Short-Term Memory (LSTM), and inter-frame convolution.

Conclusions:

  • The paper provides a comprehensive overview of video object and human action detection.
  • Understanding different detection strategies and their underlying techniques is essential for advancing the field.
  • Future advancements can build upon reviewed methods and datasets for improved video analysis capabilities.