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Reading is one of the main paths to acquire knowledge, either done traditionally on paper media or practiced on electronic devices. Efficiency varies when different reading patterns are involved. It is the objective of this research to classify reading patterns from fixation data using machine learning techniques in an attempt to understand and evaluate the reading and learning process. In our experiment,...
Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
Transit advertising provides frequent exposure to a large number of residents in different urban regions. However, traditional methods are generally manual and qualitatively based on a rough estimation, such as the number of passengers taken by a bus or functional regions covered. How to accurately put an advertisement on appropriate transportations becomes an important task for potential business...
We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of...
Spatiotemporal event sequences (STESs) are the ordered series of event types whose evolving region-based instances frequently follow each other in time and are located closeby. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. As the quality of the discovered STESs is of great importance to the domain experts...
In this paper, we propose a work flow for processing and analysing large-scale tracking data with spatio-temporal marks that uses an infrastructure for machine learning methods based on a meta-data representation of point patterns. The tracking log (IP address) of cyber security devices usually maps to geolocation and timestamp, such data is called spatiotemporal data. Existing spatio-temporal analysis...
Contrast patterns are itemsets that frequently occur in one dataset while not in another. These patterns have been successfully applied to many data mining domains, such as prediction, classification and clustering. However, none of the previous studies has considered extracting contrast patterns from different types of datasets. In this paper, we introduce a new type of contrast pattern, Conditional...
Viewpoint variation is a major challenge in video- based human action recognition. We exploit the simultaneous RGB and Depth sensing of RGB-D cameras to address this problem. Our technique capitalizes on the complementary spatio-temporal information in RGB and Depth frames of the RGB-D videos to achieve viewpoint invariant action recognition. We extract view invariant features from the dense trajectories...
Motion vectors extracted from a compressed video file can be used to track objects in the video and it could be efficient as motion vectors provide trajectory information of the objects. However, tracking objects represented by the motion vectors can be inaccuracy because of camera movement, small size sets of motion vectors acting as noise, unmoving of the object and occlusion. These are conditions...
Because of the worldwide aging population, more and more elders suffer from dementia. Nowadays, it is inconvenient and time-consuming for doctors to diagnose whether elders who live independently have dementia because lots of diagnostic questions on a checklist must be asked first, and part of them even require a long-term observation. In order to help doctors and make this diagnostic process easier,...
Traditional vehicular routing protocols cannot accurately foresee future location of each vehicle for efficient packet forwarding. Recently, the data mining approach has been applied to analyze huge vehicle trajectory data. In this paper, we propose a novel trajectory-based routing (NTR) protocol to improve the packet replication efficiency of vehicles in the Vehicular Delay Tolerant Network (VDTN)...
With the advance of mobile electronic devices and the development of positioning technology, a large volume of spatio-temopral data are collected in the form of desultorily data streams, which contain a lot of potential information. In this study, we focus on discovering the composition relationships between observation moving objects in a long period. Such research can be widely used in military...
Every day a huge amount of data is generated by users of social media platforms, like Facebook, Twitter and so on. Analyzing data posted by people interested in a given topic or event allows inferring patterns and trends about people behaviors on a very large scale. These posts are often geotagged, this way enabling mobility pattern analysis. In this work, we investigate the use of Process Mining...
The upsurge in the use of Context Aware Devices in various gadgets has led to the generation of massive mobility data. These gadgets tracks and records particulars of moving objects such as location, time, waypoints etc. in to various geographical databases. The data are recorded in the form of trajectories. Identification of the moving pattern is very much useful for setting up of the architectural...
This paper proposed a trajectory overall motion trend extraction method based on trajectory clustering, including three phases: partitioning phase, clustering phase and extracting phase. Firstly, trajectories are partitioned at characteristic points whose turn angle or accumulated turn angle exceeds the threshold value determined by minimum description length (MDL) principle. Secondly, an improved...
Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. The work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. The proposed approach uses computer vision techniques to automate data gathering from video footage. The image processing algorithm is validated using video...
Data from vehicles instrumented with GPS or other localization technologies are increasingly becoming widely available due to the investments in Connected and Automated Vehicles (CAVs) and the prevalence of personal mobile devices such as smartphones. Tracking or trajectory data from these probe vehicles are already being used in practice for travel time or speed estimation and for monitoring network...
With the development of mobile communication technology, mobile phones play a more important role in people's daily life. The user's location and its semantic are very important to Location Based Services (LBS), and this inspires a tremendous amount of research effort on analyzing large-scale trajectory data to mine these informations in the last decade. The existing researches have achieved good...
Passenger hotspots searching is essential to increase profits for taxis drivers in urban area. In this paper, we propose a two-step approach for pick-up hotspots searching. In the first step, a traveling similarity model is built to quantify the similarity of traveling behaviors. In the second step, we utilize affinity propagation and simulated annealing to identify the daily passenger hotspots in...
Rapid pace of global urbanization has posed significant challenges to urban transportation infrastructures. Existing urban transit systems suffer many well-known shortcomings, where public transits have limits on coverage areas, and fixed schedules, and private transits are expensive and fail to timely meet the demand needs. We thus envision a Cloud-Commuting system, that employs a giant pool of centralized...
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