1,212 research outputs found
Truth Discovery in Crowdsourced Detection of Spatial Events
ACKNOWLEDGMENTS This research is based upon work supported in part by the US ARL and UK Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the US ARL, the US Government, the UK Ministry of Defense or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.Peer reviewedPostprin
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Recognition of Crowd Behavior from Mobile Sensors with Pattern Analysis and Graph Clustering Methods
Mobile on-body sensing has distinct advantages for the analysis and
understanding of crowd dynamics: sensing is not geographically restricted to a
specific instrumented area, mobile phones offer on-body sensing and they are
already deployed on a large scale, and the rich sets of sensors they contain
allows one to characterize the behavior of users through pattern recognition
techniques.
In this paper we present a methodological framework for the machine
recognition of crowd behavior from on-body sensors, such as those in mobile
phones. The recognition of crowd behaviors opens the way to the acquisition of
large-scale datasets for the analysis and understanding of crowd dynamics. It
has also practical safety applications by providing improved crowd situational
awareness in cases of emergency.
The framework comprises: behavioral recognition with the user's mobile
device, pairwise analyses of the activity relatedness of two users, and graph
clustering in order to uncover globally, which users participate in a given
crowd behavior. We illustrate this framework for the identification of groups
of persons walking, using empirically collected data.
We discuss the challenges and research avenues for theoretical and applied
mathematics arising from the mobile sensing of crowd behaviors
A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process
Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
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