DocumentCode :
2142825
Title :
Toward event recognition using dynamic trajectory analysis and prediction
Author :
Piciarelli, C. ; Foresti, G.L.
Author_Institution :
Udine Univ., Italy
fYear :
2005
fDate :
7-8 June 2005
Firstpage :
131
Lastpage :
134
Abstract :
In this paper we propose a trajectory analysis method suited for event recognition. The method works online, in the sense that it can process the data as they are acquired by the sensors and it is dynamic, since it adapts the results to the changes in the patterns of activity. For each class of objects detected by the system, the proposed method groups trajectories with common features in clusters and, based on the identification of common prefixes in the clusters, can make probabilistic predictions on the possible future positions of a moving object. This analysis can give valuable information to an event recognition system for the identification of anomalous events.
Keywords :
image recognition; object detection; pattern clustering; prediction theory; probability; anomalous event identification; dynamic trajectory analysis; event recognition; moving object detection; probabilistic prediction; sensor data processing; trajectory prediction;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Imaging for Crime Detection and Prevention, 2005. ICDP 2005. The IEE International Symposium on
ISSN :
0537-9989
Print_ISBN :
0-86341-535-0
Type :
conf
DOI :
10.1049/ic:20050084
Filename :
1515878
Link To Document :
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