2,855 research outputs found
Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied
in diverse domains including, climate science, social sciences, neuroscience,
epidemiology, transportation, mobile health, and Earth sciences.
Spatio-temporal data differs from relational data for which computational
approaches are developed in the data mining community for multiple decades, in
that both spatial and temporal attributes are available in addition to the
actual measurements/attributes. The presence of these attributes introduces
additional challenges that needs to be dealt with. Approaches for mining
spatio-temporal data have been studied for over a decade in the data mining
community. In this article we present a broad survey of this relatively young
field of spatio-temporal data mining. We discuss different types of
spatio-temporal data and the relevant data mining questions that arise in the
context of analyzing each of these datasets. Based on the nature of the data
mining problem studied, we classify literature on spatio-temporal data mining
into six major categories: clustering, predictive learning, change detection,
frequent pattern mining, anomaly detection, and relationship mining. We discuss
the various forms of spatio-temporal data mining problems in each of these
categories.Comment: Accepted for publication at ACM Computing Survey
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
Traffic prediction plays an essential role in intelligent transportation
system. Accurate traffic prediction can assist route planing, guide vehicle
dispatching, and mitigate traffic congestion. This problem is challenging due
to the complicated and dynamic spatio-temporal dependencies between different
regions in the road network. Recently, a significant amount of research efforts
have been devoted to this area, especially deep learning method, greatly
advancing traffic prediction abilities. The purpose of this paper is to provide
a comprehensive survey on deep learning-based approaches in traffic prediction
from multiple perspectives. Specifically, we first summarize the existing
traffic prediction methods, and give a taxonomy. Second, we list the
state-of-the-art approaches in different traffic prediction applications.
Third, we comprehensively collect and organize widely used public datasets in
the existing literature to facilitate other researchers. Furthermore, we give
an evaluation and analysis by conducting extensive experiments to compare the
performance of different methods on a real-world public dataset. Finally, we
discuss open challenges in this field.Comment: to be published in IEEE Transactions on Intelligent Transportation
System
Crowded Scene Analysis: A Survey
Automated scene analysis has been a topic of great interest in computer
vision and cognitive science. Recently, with the growth of crowd phenomena in
the real world, crowded scene analysis has attracted much attention. However,
the visual occlusions and ambiguities in crowded scenes, as well as the complex
behaviors and scene semantics, make the analysis a challenging task. In the
past few years, an increasing number of works on crowded scene analysis have
been reported, covering different aspects including crowd motion pattern
learning, crowd behavior and activity analysis, and anomaly detection in
crowds. This paper surveys the state-of-the-art techniques on this topic. We
first provide the background knowledge and the available features related to
crowded scenes. Then, existing models, popular algorithms, evaluation
protocols, as well as system performance are provided corresponding to
different aspects of crowded scene analysis. We also outline the available
datasets for performance evaluation. Finally, some research problems and
promising future directions are presented with discussions.Comment: 20 pages in IEEE Transactions on Circuits and Systems for Video
Technology, 201
Human Action Recognition and Prediction: A Survey
Derived from rapid advances in computer vision and machine learning, video
analysis tasks have been moving from inferring the present state to predicting
the future state. Vision-based action recognition and prediction from videos
are such tasks, where action recognition is to infer human actions (present
state) based upon complete action executions, and action prediction to predict
human actions (future state) based upon incomplete action executions. These two
tasks have become particularly prevalent topics recently because of their
explosively emerging real-world applications, such as visual surveillance,
autonomous driving vehicle, entertainment, and video retrieval, etc. Many
attempts have been devoted in the last a few decades in order to build a robust
and effective framework for action recognition and prediction. In this paper,
we survey the complete state-of-the-art techniques in the action recognition
and prediction. Existing models, popular algorithms, technical difficulties,
popular action databases, evaluation protocols, and promising future directions
are also provided with systematic discussions
A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data
In the past several years, social media (e.g., Twitter and Facebook) has been
experiencing a spectacular rise and popularity, and becoming a ubiquitous
discourse for content sharing and social networking. With the widespread of
mobile devices and location-based services, social media typically allows users
to share whereabouts of daily activities (e.g., check-ins and taking photos),
and thus strengthens the roles of social media as a proxy to understand human
behaviors and complex social dynamics in geographic spaces. Unlike conventional
spatiotemporal data, this new modality of data is dynamic, massive, and
typically represented in stream of unstructured media (e.g., texts and photos),
which pose fundamental representation, modeling and computational challenges to
conventional spatiotemporal analysis and geographic information science. In
this paper, we describe a scalable computational framework to harness massive
location-based social media data for efficient and systematic spatiotemporal
data analysis. Within this framework, the concept of space-time trajectories
(or paths) is applied to represent activity profiles of social media users. A
hierarchical spatiotemporal data model, namely a spatiotemporal data cube
model, is developed based on collections of space-time trajectories to
represent the collective dynamics of social media users across aggregation
boundaries at multiple spatiotemporal scales. The framework is implemented
based upon a public data stream of Twitter feeds posted on the continent of
North America. To demonstrate the advantages and performance of this framework,
an interactive flow mapping interface (including both single-source and
multiple-source flow mapping) is developed to allow real-time, and interactive
visual exploration of movement dynamics in massive location-based social media
at multiple scales
Space-Time Representation of People Based on 3D Skeletal Data: A Review
Spatiotemporal human representation based on 3D visual perception data is a
rapidly growing research area. Based on the information sources, these
representations can be broadly categorized into two groups based on RGB-D
information or 3D skeleton data. Recently, skeleton-based human representations
have been intensively studied and kept attracting an increasing attention, due
to their robustness to variations of viewpoint, human body scale and motion
speed as well as the realtime, online performance. This paper presents a
comprehensive survey of existing space-time representations of people based on
3D skeletal data, and provides an informative categorization and analysis of
these methods from the perspectives, including information modality,
representation encoding, structure and transition, and feature engineering. We
also provide a brief overview of skeleton acquisition devices and construction
methods, enlist a number of public benchmark datasets with skeleton data, and
discuss potential future research directions.Comment: Our paper has been accepted by the journal Computer Vision and Image
Understanding, see
http://www.sciencedirect.com/science/article/pii/S1077314217300279, Computer
Vision and Image Understanding, 201
Urban flows prediction from spatial-temporal data using machine learning: A survey
Urban spatial-temporal flows prediction is of great importance to traffic
management, land use, public safety, etc. Urban flows are affected by several
complex and dynamic factors, such as patterns of human activities, weather,
events and holidays. Datasets evaluated the flows come from various sources in
different domains, e.g. mobile phone data, taxi trajectories data, metro/bus
swiping data, bike-sharing data and so on. To summarize these methodologies of
urban flows prediction, in this paper, we first introduce four main factors
affecting urban flows. Second, in order to further analysis urban flows, a
preparation process of multi-sources spatial-temporal data related with urban
flows is partitioned into three groups. Third, we choose the spatial-temporal
dynamic data as a case study for the urban flows prediction task. Fourth, we
analyze and compare some well-known and state-of-the-art flows prediction
methods in detail, classifying them into five categories: statistics-based,
traditional machine learning-based, deep learning-based, reinforcement
learning-based and transfer learning-based methods. Finally, we give open
challenges of urban flows prediction and an outlook in the future of this
field. This paper will facilitate researchers find suitable methods and open
datasets for addressing urban spatial-temporal flows forecast problems
Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions
We present a commonsense, qualitative model for the semantic grounding of
embodied visuo-spatial and locomotive interactions. The key contribution is an
integrative methodology combining low-level visual processing with high-level,
human-centred representations of space and motion rooted in artificial
intelligence. We demonstrate practical applicability with examples involving
object interactions, and indoor movement.Comment: to appear in: ICCV 2017 Workshop - Vision in Practice on Autonomous
Robots (ViPAR), International Conference on Computer Vision (ICCV), Venice,
Ital
Adaptive modeling of urban dynamics during ephemeral event via mobile phone traces
The communication devices have produced digital traces for their users either
voluntarily or not. This type of collective data can give powerful indications
that are affecting the urban systems design and development. In this study
mobile phone data during Armada event is investigated. Analyzing mobile phone
traces gives conceptual views about individuals densities and their mobility
patterns in the urban city. The geo-visualization and statistical techniques
have been used for understanding human mobility collectively and individually.
The undertaken substantial parameters are inter-event times, travel distances
(displacements) and radius of gyration. They have been analyzed and simulated
using computing platform by integrating various applications for huge database
management, visualization, analysis, and simulation. Accordingly, the general
population pattern law has been extracted. The study contribution outcomes have
revealed both the individuals densities in static perspective and individuals
mobility in dynamic perspective with multi levels of abstraction (macroscopic,
mesoscopic, microscopic)
The Long Road to Computational Location Privacy: A Survey
The widespread adoption of continuously connected smartphones and tablets
developed the usage of mobile applications, among which many use location to
provide geolocated services. These services provide new prospects for users:
getting directions to work in the morning, leaving a check-in at a restaurant
at noon and checking next day's weather in the evening are possible right from
any mobile device embedding a GPS chip. In these location-based applications,
the user's location is sent to a server, which uses them to provide contextual
and personalised answers. However, nothing prevents the latter from gathering,
analysing and possibly sharing the collected information, which opens the door
to many privacy threats. Indeed, mobility data can reveal sensitive information
about users, among which one's home, work place or even religious and political
preferences. For this reason, many privacy-preserving mechanisms have been
proposed these last years to enhance location privacy while using geolocated
services. This article surveys and organises contributions in this area from
classical building blocks to the most recent developments of privacy threats
and location privacy-preserving mechanisms. We divide the protection mechanisms
between online and offline use cases, and organise them into six categories
depending on the nature of their algorithm. Moreover, this article surveys the
evaluation metrics used to assess protection mechanisms in terms of privacy,
utility and performance. Finally, open challenges and new directions to address
the problem of computational location privacy are pointed out and discussed.Comment: IEEE Communications Surveys & Tutorial
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