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Trajectory clustering in road network environment
2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009This paper proposes a new trajectory clustering scheme for objects moving on road networks. A trajectory on road networks can be defined as a sequence of road segments a moving object has passed by. We first propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments.
Jung-Im Won +3 more
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A Framework for Trajectory Clustering
2009The increasing availability of huge amounts of "thin" data, i.e. data pertaining to time and positions generated by different sources with a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatio-temporal data collections. Mining such amounts of data is challenging, since the possibility of extracting useful information
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Fast and Accurate Trajectory Streams Clustering
2011Trajectory data streams are huge amounts of data pertaining to time and position of moving objects. They are continuously generated by different sources exploiting a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind ...
Masciari, Elio
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Robust Trajectory Clustering for Motion Segmentation
2013 IEEE International Conference on Computer Vision, 2013Due to occlusions and objects' non-rigid deformation in the scene, the obtained motion trajectories from common trackers may contain a number of missing or mis-associated entries. To cluster such corrupted point based trajectories into multiple motions is still a hard problem.
Feng Shi 0002 +3 more
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Online Clustering of Trajectories in Road Networks
2020 21st IEEE International Conference on Mobile Data Management (MDM), 2020The ubiquity of GPS-enabled smartphones and automotive navigation systems allows to monitor and collect massive streams of trajectory data in real-time. This enables real-time analyses on mobility data in urban settings, which in turn have the potential to substantially improve traffic conditions, analyze congested areas, detect events in (quasi) real ...
Ticiana L. Coelho da Silva +4 more
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Fusion of trajectory clusters for situation assessment
2006 9th International Conference on Information Fusion, 2006In this paper, we address the problem of identifying anomalous events in the context of a multi sensor surveillance system. Targets' trajectories are analysed and compared to common patterns of activity represented as clusters of trajectories. Here we extend our previous work to cater for observations provided by multiple cameras observing the same ...
SNIDARO, Lauro +2 more
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Lifting Trajectories for Effective Clustering
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010The increasing availability of huge amounts of data pertaining to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this ...
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Unsupervised Fuzzy Clustering for Trajectory Analysis
2007 IEEE International Conference on Image Processing, 2007We propose an unsupervised fuzzy approach for motion trajectory clustering. The proposed approach is divided into three main steps: first Mean-shift is used for local mode seeking by analyzing trajectory data over multiple feature spaces. This step generates a set of tentative clusters.
Nadeem Anjum, Andrea Cavallaro
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Extraction and clustering of motion trajectories in video
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004A system is described that tracks moving objects in a video dataset so as to extract a representation of the objects' 3D trajectories. The system then finds hierarchical clusters of similar trajectories in the video dataset. Objects' motion trajectories are extracted via an EKF formulation that provides each object's 3D trajectory up to a constant ...
Dan Buzan, Stan Sclaroff, George Kollios
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Predicting Next Locations with Object Clustering and Trajectory Clustering
2015Next location prediction is of great importance for many location based applications. In many cases, understanding the similarity between objects and the similarity between trajectories may lead to more accurate predictions. In this paper, we propose two novel models exploiting these two types of similarities respectively. The first model, named object-
Meng Chen 0003 +2 more
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