Paper Info
Title
A Completely Autonomous System That Learns Anomalous Movements In Advanced Videosurveillance Applications
Abstract
This paper describes an automatic real-time video surveillance system, capable of autonomously learning and signaling anomalous activities of moving objects. To obtain these capabilities, an improved version of the Altruistic Vector Quantization algorithm (AVQ) is proposed. The modified AVQ automatically evaluates the number of trajectory prototypes, and improves the representativeness of the prototypes themselves, so the visual events can be easily and accurately classified. Anomalous behaviors are detected if visual trajectories deviate from the self-learned representations of "typical" behaviors. The system has been implemented by means of standard PCs and TV cameras, and has been tested in many real outdoor contexts in different conditions (night and day). Currently it is used to monitor the storage areas of British Airways at the airport of Peretola (Florence, Italy), and some access gates of Autostrade per l'Italia S.p.A. (the main Italian highways company). If the camera field-of-view is changed, the system automatically re-learns new "typical" behaviors and accurately detects anomalous events.
Year
DOI
Venue
2005
10.1109/ICIP.2005.1530123
2005 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), VOLS 1-5
Keywords
Field
DocType
real time,field of view
Object detection,Computer vision,Computer science,Representativeness heuristic,Vector quantisation,Vector quantization,Artificial intelligence,Autonomous system (mathematics),Trajectory
Conference
ISSN
Citations 
PageRank 
1522-4880
15
0.97
References 
Authors
3
2
Name
Order
Citations
PageRank
Alessandro Mecocci16014.38
Massimo Pannozzo2150.97