File(s) under permanent embargo
The LV dataset: a realistic surveillance video dataset for abnormal event detection
conference contribution
posted on 2017-01-01, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun LiIn recent years, designing and testing video anomaly detection methods have focused on synthetic or unrealistic sequences. This has mainly four drawbacks: 1) events are controlled and predictable because they are usually performed by actors; 2) environmental conditions, e.g. camera motion and illumination, are usually ideal thus realistic conditions are not well reflected; 3) events are usually short and repetitive; and 4) the material is captured from scenarios that do not necessarily match the testing scenarios. This leads us to propose a new rich collection of realistic videos captured by surveillance cameras in challenging environmental conditions, the Live Videos (LV) dataset. We explore the performance of a number of state-of-the-art video anomaly detection methods on the LV dataset. Our results confirm the need to design methods that are capable of handling realistic videos captured by surveillance cameras with acceptable processing times. The proposed LV dataset, thus, will facilitate the design and testing of such new methods.
History
Event
European Association for Biometrics. Workshop (5th : 2017 : Coventry, Eng.)Series
European Association for Biometrics WorkshopPagination
1 - 6Publisher
Institute of Electrical and Electronics EngineersLocation
Coventry, Eng.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2017-04-04End date
2017-04-05ISBN-13
9781509057917Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2017, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IWBF 2017 : Proceedings of the 2017 5th International Workshop on Biometrics and ForensicsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC