A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining

Author(s):  
Chengcui Zhang

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

Author(s):  
Chengcui Zhang

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.


Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang

Spatio-temporal data mining is an emerging area with increasing importance in a variety of applications, such as homeland security, mobile services, surveillance systems, and health monitoring applications. However, mining in spatio-temporal databases is still in its infancy. Existing work on spatio-temporal data mining has mainly focused on three types of patterns: evolution patterns of natural phenomena, frequent movements of objects over time, and space-time clusters. While there has been much research on association rule mining on transactional, spatial, and temporal data, there is little literature on finding interesting associations in spatio-temporal data. In this chapter, we introduce the early attempts at spatio-temporal data mining and review the techniques to discover various interesting spatio-temporal patterns. This is followed by a review of the traditional association rules mining algorithms and their variants on transactional data, temporal data, and spatial data.


2007 ◽  
Vol 18 (3) ◽  
pp. 255-279 ◽  
Author(s):  
P. Compieta ◽  
S. Di Martino ◽  
M. Bertolotto ◽  
F. Ferrucci ◽  
T. Kechadi

2013 ◽  
Vol 60 (2) ◽  
pp. 217-229 ◽  
Author(s):  
A. S. Merdith ◽  
T. C. W. Landgrebe ◽  
A. Dutkiewicz ◽  
R. D. Müller

2018 ◽  
Vol 51 (4) ◽  
pp. 1-41 ◽  
Author(s):  
Gowtham Atluri ◽  
Anuj Karpatne ◽  
Vipin Kumar

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