Name
Title | ||
|---|---|---|
Online adaptive dictionary learning and weighted sparse coding for abnormality detection |
Abstract | ||
|---|---|---|
This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2013.6738032 | ICIP |
Keywords | Field | DocType |
abnormality analysis,learning (artificial intelligence),abnormality detection,sparse coding,adaptive learning,video coding,online adaptive dictionary learning,dictionary learning,condensed dictionary,weighted sparse coding,video surveillance,learning artificial intelligence | Adaptability,Dictionary learning,K-SVD,Pattern recognition,Neural coding,Computer science,Abnormality,Coding (social sciences),Speech recognition,Artificial intelligence,Abnormality detection,Weighted space | Conference |
ISSN | Citations | PageRank |
1522-4880 | 7 | 0.44 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Han | 1 | 7 | 0.78 |
| Ruiqing Fu | 2 | 20 | 5.50 |
| Su-zhen Wang | 3 | 11 | 3.67 |
| Xinyu Wu | 4 | 515 | 80.44 |