Paper Info
Title
Mid-Level Feature Set For Specific Event And Anomaly Detection In Crowded Scenes
Abstract
In this paper we propose a system for automatic detection of specific events and abnormal behaviors in crowded scenes. In particular, we focus on the parametrization by proposing a set of mid-level spatio-temporal features that successfully model the characteristic motion of typical events in crowd behaviors. Furthermore, due to the fact that some features are more suitable than others to model specific events of interest, we also present an automatic process for feature selection. Our experiments prove that the suggested feature set works successfully for both explicit event detection and distance-based anomaly detection tasks. The results on PETS for explicit event detection are generally better than those previously reported. Regarding anomaly detection, the proposed method performance is comparable to those of state-of-the-art method for PETS and substantially better than that reported for Web dataset.
Year
Venue
Keywords
2013
2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
Machine Vision, Video processing, Video surveillance, Crowded environments, Clutter environment, Motion analysis
Field
DocType
ISSN
Computer vision,Anomaly detection,Data mining,Pattern recognition,Feature selection,Parametrization,Computer science,Feature set,Artificial intelligence
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Fernando de-la-Calle-Silos100.68
Iván González-Díaz2469.51
Fernando Díaz-de-María320132.14