Avi Singh • 5/27/2015

Recognizing Human Activities with Kinect - Choosing a temporal model

Read Original

This technical article discusses the selection of temporal models for sequence classification in a human activity recognition project using RGBD data from a Kinect. It compares popular models like Hidden Markov Models (HMMs), Maximum Entropy Markov Models (MEMMs), and Conditional Random Fields (CRFs), explaining their advantages and disadvantages. The content is based on a graduate-level machine learning course project and serves as an introduction for those starting with sequence classification.

0 comments
#Machine Learning #Kinect #Sequence Classification
Recognizing Human Activities with Kinect - Choosing a temporal model

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
Quoting Thariq Shihipar
Simon Willison • 2 votes
2
Using Browser Apis In React Practical Guide
Jivbcoop • 2 votes
3
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
4
Top picks — 2026 January
Paweł Grzybek • 1 votes
5
In Praise of –dry-run
Henrik Warne • 1 votes
6
Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years On
Ferenc Huszár • 1 votes
7
Vibe coding your first iOS app
William Denniss • 1 votes
8
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
9
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes