A review of moving object trajectory clustering algorithms

2016 ◽  
Vol 47 (1) ◽  
pp. 123-144 ◽  
Author(s):  
Guan Yuan ◽  
Penghui Sun ◽  
Jie Zhao ◽  
Daxing Li ◽  
Canwei Wang
Keyword(s):  
Clustering Algorithms ◽  
Moving Object ◽  
Trajectory Clustering ◽  
Moving Object Trajectory

scholarly journals Design of intelligent acquisition system for moving object trajectory data under cloud computing

2021 ◽  
Vol 30 (1) ◽  
pp. 763-773
Author(s):  
Yang Zhang ◽  
Abhinav Asthana ◽  
Sudeep Asthana ◽  
Shaweta Khanna ◽  
Ioan-Cosmin Mihai
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Hot Spot ◽  
Moving Object ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Spot Area ◽  
Time Period ◽  
Moving Object Trajectory

Abstract In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based DBSCAN algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.

scholarly journals PIXEL-BY-PIXEL ESTIMATION OF SCENE MOTION IN VIDEO

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 61-65 ◽  
Author(s):  
A. G. Tashlinskii ◽  
P. V. Smirnov ◽  
M. G. Tsaryov
Keyword(s):  
Recursive Estimation ◽  
Stochastic Gradient ◽  
Moving Object ◽  
Stochastic Gradient Descent ◽  
Processing Efficiency ◽  
Estimated Parameters ◽  
Gradient Descent Algorithm ◽  
The Right ◽  
Moving Object Trajectory ◽  
Inter Frame

The paper considers the effectiveness of motion estimation in video using pixel-by-pixel recurrent algorithms. The algorithms use stochastic gradient decent to find inter-frame shifts of all pixels of a frame. These vectors form shift vectors’ field. As estimated parameters of the vectors the paper studies their projections and polar parameters. It considers two methods for estimating shift vectors’ field. The first method uses stochastic gradient descent algorithm to sequentially process all nodes of the image row-by-row. It processes each row bidirectionally i.e. from the left to the right and from the right to the left. Subsequent joint processing of the results allows compensating inertia of the recursive estimation. The second method uses correlation between rows to increase processing efficiency. It processes rows one after the other with the change in direction after each row and uses obtained values to form resulting estimate. The paper studies two criteria of its formation: gradient estimation minimum and correlation coefficient maximum. The paper gives examples of experimental results of pixel-by-pixel estimation for a video with a moving object and estimation of a moving object trajectory using shift vectors’ field.

Mining Partners in Trajectories

2020 ◽  
Vol 16 (1) ◽  
pp. 22-38
Author(s):  
Diego Vilela Monteiro ◽  
Rafael Duarte Coelho dos Santos ◽  
Karine Reis Ferreira
Keyword(s):  
Moving Objects ◽  
R Package ◽  
Moving Object ◽  
Spatiotemporal Data ◽  
Data Preparation ◽  
Trajectory Data ◽  
Gps Satellites ◽  
Object Trajectories ◽  
Moving Object Trajectory ◽  
Moving Object Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

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