DMCA
Video parsing for abnormality detection (2011)
| Venue: | In ICCV |
| Citations: | 8 - 1 self |
BibTeX
@INPROCEEDINGS{́11videoparsing,
author = {Borislav Antic ́},
title = {Video parsing for abnormality detection},
booktitle = {In ICCV},
year = {2011},
pages = {2415--2422}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Detecting abnormalities in video is a challenging prob-lem since the class of all irregular objects and behaviors is infinite and thus no (or by far not enough) abnormal train-ing samples are available. Consequently, a standard set-ting is to find abnormalities without actually knowing what they are because we have not been shown abnormal exam-ples during training. However, although the training data does not define what an abnormality looks like, the main paradigm in this field is to directly search for individual ab-normal local patches or image regions independent of an-other. To address this problem we parse video frames by estab-lishing a set of hypotheses that jointly explain all the fore-ground while, at same time, trying to find normal training samples that explain the hypotheses. Consequently, we can avoid a direct detection of abnormalities. They are discov-ered indirectly as those hypotheses which are needed for covering the foreground without finding an explanation by normal samples for themselves. We present a probabilistic model that localizes abnormalities using statistical infer-ence. On the challenging dataset of [15] it outperforms the state-of-the-art by 7 % to achieve a frame-based abnormal-ity classification performance of 91 % and the localization performance improves by 32 % to 76%. 1.
Keyphrases
abnormality detection statistical infer-ence training data abnormal train-ing sample localization performance improves image region irregular object standard set-ting individual ab-normal local patch abnormal exam-ples video frame direct detection probabilistic model challenging dataset normal training sample normal sample frame-based abnormal-ity classification performance main paradigm
