PPT-Trajectory Clustering for Motion Prediction

Author : conchita-marotz | Published Date : 2017-11-07

Cynthia Sung Dan Feldman Daniela Rus October 8 2012 Trajectory Clustering 1 Background Noise Sampling frequency Inaccurate control SLAM Ranganathan and Dellaert

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Trajectory Clustering for Motion Prediction: Transcript


Cynthia Sung Dan Feldman Daniela Rus October 8 2012 Trajectory Clustering 1 Background Noise Sampling frequency Inaccurate control SLAM Ranganathan and Dellaert 2011 Cummins and Newman 2009 . Road. . Networks. Renchu . Song, . . Weiwei . Sun, . . Fudan. University. Baihua Zheng, Singapore Management University. . Yu . Zheng, . Microsoft Research, . Beijing. Background. Big Data. Huge volume of spatial trajectories cause heavy burden to data storage and data process. USING. MIX ZONES MODEL. THIRD REVIEW. BATCH NO:19. PROJECT. . GUIDE. :. Ms .S. .. NIVETHA. . M.E. .. . AP/CSE. R.LATHAA(81210132041),. R.MANGAIYARKARASI(81210132045),. K.MONICA(81210132051),. Alla . Petrakova. Work overview. Becoming familiar with Motion Pattern algorithms described in:. Similarity Invariant Classification of Events by KL Divergence Minimization . by . Khokhar. , . Saleemi. --Presented By . Sudheer. . Chelluboina. .. Professor: . Dr.Maggie. Dunham. Contents . Outline of Paper. Introduction . Index Structures. Due to rapid increase in the use of location based services applications, large amount of location data of moving object is recorded. Because of that efficient indexing techniques are required to manage these large amounts of trajectory data. All index structures are focused on either indexing past, current and future locations. Every indexing structure or techniques discussed in this paper will make simpler indexing or it will increase the overall query processing performance. . : Trajectory Classification Using Hierarchical Region-Based and Trajectory-Based Clustering. Jae-Gil Lee, . Jiawei. Han, . Xiaolei. Li, Hector Gonzalez. University of Illinois at Urbana-Champaign. VLDB 2008. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . . USING HIERARHICAL REGION-BASED . . AND . . TRAJECTORY-BASED CLUSTERING . . JaeGil. Lee, . Jiawei. Han, . . Xiaolei. . Li, . Hector Gonzalez. Department of Computer Science. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Ke. Deng, . Kexin. . Xie. , Kevin . Zheng. and . Xiaofang. Zhou. 9/06/2016. 1. 2. Chapter Overview. Trajectory Query Classification. Trajectory Similarity Measure. Trajectory Data Index. Trajectory Query Processing. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because . CHINA. Submitted by the expert from China. Informal document . GRE-89-13. (89th GRE, 24 to 27 October 2023, . agenda . item 6 (. a. )) . Scenarios:. When driving on adverse road condition, narrow, uneven, with smalls stones or sharp metal..

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