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 effective...

In this paper, we propose a graphical data model for specifying spatio-temporal semantics of video data. The proposed model segments a video clip into subsegments consisting of objects. Each object is detected and recognized, and the relevant information of each object i s recorded. The motions of objects are modeled through their relative spatial relationships as t ime evolves. Based on the se...

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was three different ocean datasets: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of natural system – across the spatial (between individual s...

When facing spatio-temporal datasets of massive size, the aggravated computational burden can often lead to failures in the implementations of traditional spatial statistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model where the spatial dependence is approximated by a Gaussian Markov random field while the temporal correlation is desc...

Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporally segmented sequence is fed to a learned model, which could require the prediction to wait until the full sequence is available to an auxiliary task that performs the temporal segmentation. This causes a delay in pre...

This paper presents ST-Hadoop; the first full-fledged opensource MapReduce framework with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to Hadoop and SpatialHadoop that injects spatio-temporal data awareness inside each of their layers, mainly, language, indexing, and operations layers. In the language layer, ST-Hadoop provides built in spatio-temporal data t...

The brain functions as a spatio-temporal information processing machine and deals extremely well with spatio-temporal data. Spatioand spectro-temporal data (SSTD) are the most common data collected to measure brain signals and brain activities, along with the recently obtained gene and protein data. Yet, there are no computational models to integrate all these different types of data into a sin...

Spatio-temporal uncertainty is a special feature of moving objects due to the inability of precisely capturing or predicting their continuously changing locations. Indeterminate locations of moving objects at time instants add uncertainty to their topological relationships. Spatio-temporal uncertainty is important in many applications, for example, to determine whether two moving objects could ...

While there are many proposals for spatio-temporal data models and query languages, there is a lack of research into application development using spatio-temporal database systems. This paper seeks to redress the balance by exploring how to support database programming for spatio-temporal object databases, with specific reference to the Tripod spatio-temporal OODBMS.

Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location– Based Services (LBS). To achieve high quality for such services, spatio– temporal data mining techniques are needed. In this paper, we describe experiences with spatio–temporal rule mining in a Danish...