Material Detail

A tutorial on deep and unsupervised feature learning for activity recognition

A tutorial on deep and unsupervised feature learning for activity recognition

This video was recorded at Workshop on Gesture Recognition and launching of a benchmark program. Recognition of human activity from video data is a challenging problem that has received an increasing amount of attention from the computer vision community in recent years. Currently the best performing methods at this task are based on engineered descriptors with explicit local geometric cues and other heuristics. Until very recently, learning has not played a major role until the classification stage, at which point much of the input is lost. It has been shown that learning features in a supervised, unsupervised, or semi-supervised setting can improve performance in other vision tasks, but most of these works have concentrated on static images rather than video. In this tutorial, we will... Show More
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden