By Topic

Unexpected Human Behavior Recognition in Image Sequences Using Multiple Features

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)
Zweng, A. ; Inst. of Comput. Aided Autom., Vienna Univ. of Technol., Vienna, Austria ; Kampel, M.

This paper presents a novel approach for unexpected behavior recognition in image sequences with attention to high density crowd scenes. Due to occlusions, object-tracking in such scenes is challenging and in cases of low resolution or poor image quality it is not robust enough to efficiently detect abnormal behavior. The wide variety of possible actions performed by humans and the problem of occlusions makes action recognition unsuitable for behavior recognition in high density crowd scenes. The novel approach, which is presented in this paper uses features based on motion information instead of detecting actions or events in order to detect abnormality. Experiments demonstrate the potentials of the approach.

Published in:

Pattern Recognition (ICPR), 2010 20th International Conference on

Date of Conference:

23-26 Aug. 2010