DMCA
Event-based analysis of video (2001)
| Venue: | In Proc. CVPR |
| Citations: | 145 - 2 self |
BibTeX
@INPROCEEDINGS{Zelnik-manor01event-basedanalysis,
author = {Lihi Zelnik-manor and Michal Irani},
title = {Event-based analysis of video},
booktitle = {In Proc. CVPR},
year = {2001},
pages = {123--130}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Dynamic events can be regarded as long-term temporal objects, which are characterized by spatiotemporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This measure is non-parametric and can thus handle a wide range of dynamic events. Having an event-based distance measure between sequences, we use it for a variety of tasks, including: (i) event-based search and indexing into long video sequences (for “intelligent fast forward”), (ii) temporal segmentation of long video sequences based on behavioral content, and (iii) clustering events within long video sequence into event-consistent sub-sequences (i.e., into event-consistent “clusters”). These tasks are performed without prior knowledge of the types of events, their models, or their temporal extents. Our simple event representation and associated distance measure supports event-based search and indexing even when only one short example-clip is available. However, when multiple example-clips of the same event are available (either as a result of the clustering process, or supplied manually), these can be used to refine the event representation, the associated distance measure, and accordingly the quality of the detection and clustering process. 1
Keyphrases
long video sequence event-based analysis event-based search dynamic event distance measure behavioral content video sequence different length event representation long-term temporal object event-consistent cluster wide range spatiotemporal feature simple event representation temporal extent clustering process event-consistent sub-sequences multiple example-clips event-based distance measure prior knowledge short example-clip multiple temporal scale temporal segmentation simple statistical distance measure
