By Topic

Context-aware activity recognition by Markov logic networks of trained weights

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Gowun Jeong ; Dept. of CS, KAIST, Daejeon, South Korea ; Yang, H.S.

One of the general objectives of visual surveillance is to recognise abnormal activities from images. Current object detection/tracking techniques cannot directly classify such activities as fighting and snatching, while they reliably recognise primitive actions, such as walking and running. We represent each target activity as ground, weighted and undirected trees, Markov logic networks (MLNs), starting with primitive actions at the bottom and activities on top, using Horn clauses. The likelihood of one ground activity at root gives a reliable probability that the event actually happens. Computing such a probability could be intractable unless the truth values of all the nodes in a given network are known in advance. This study proposes two methods to infer such unknown values in exploitative and explorative manners. An additional modification of MLNs is also considered to improve accuracy of recognition. The experiments by means of unknown value inference methods and modification of MLNs present that these approaches overcome several well-known limitations that the conventional researches have experienced.

Published in:

Virtual Systems and Multimedia (VSMM), 2010 16th International Conference on

Date of Conference:

20-23 Oct. 2010