Results 1 - 10 of 12

Detecting Commuting Patterns by Clustering Subtrajectories

by Kevin Buchin, Maike Buchin, Joachim Gudmundsson, Maarten Löffler, Jun Luo , 2008
"... In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in spee ..."
Abstract - Cited by 30 (14 self) - Add to MetaCart
In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences

Finding frequent sub-trajectories with time constraints

by Xin Huang , Jun Luo , Xin Wang - In KDD UrbComp. ACM, 2013
"... ABSTRACT With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. Trajectory pattern mining has received a lot of attention in recent years. Frequent sub-trajectories, in particular, might contain v ..."
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very usable knowledge. In this paper, we define a new trajectory pattern called frequent sub-trajectories with time constraints (FSTTC) that requires not only the same continuous location sequence but also the similar staying time in each location. We present a two-phase approach to find FSTTCs based

Finding Long and Similar Parts of Trajectories

by Kevin Buchin, Maike Buchin, Marc Van Kreveld, Kevin Buchin, Maike Buchin, Marc Van Kreveld, Jun Luo , 2011
"... A natural time-dependent similarity measure for two trajectories is their average distance at corresponding times. We give algorithms for computing the most similar subtrajectories under this measure, assuming the two trajectories are given as two polygonal, possibly self-intersecting lines with tim ..."
Abstract - Cited by 11 (4 self) - Add to MetaCart
A natural time-dependent similarity measure for two trajectories is their average distance at corresponding times. We give algorithms for computing the most similar subtrajectories under this measure, assuming the two trajectories are given as two polygonal, possibly self-intersecting lines

Trajectory Clustering: A Partition-and-Group Framework

by Jae-gil Lee, Jiawei Han - In SIGMOD , 2007
"... Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especiall ..."
Abstract - Cited by 168 (12 self) - Add to MetaCart
Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications

XIANGQIAN XUE,

by Yan Sun, Ling Chen
"... Trajectory clustering can predict moving trend of objects effectively. The traditional trajectory clustering algorithms take moving trajectory of a whole object as a research object, which will lose similar subtrajectories. However, in practical applications, such as in RFID system, the users may on ..."
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Trajectory clustering can predict moving trend of objects effectively. The traditional trajectory clustering algorithms take moving trajectory of a whole object as a research object, which will lose similar subtrajectories. However, in practical applications, such as in RFID system, the users may

Segmented trajectory based indexing and retrieval of video data

by Faisal I. Bashir, Ashfaq A. Khokhur, Dan Schonfeld - Proc. ICIP 2003 , 2003
"... In this paper. we present a novel principal component analysis (PCA) based approach towards modeling the object trajectory in a video clip. An eigenspace decomposition of high-dimensional trajectory data leads to very compact representation, which is then used as indexing structure. To cutback on PC ..."
Abstract - Cited by 25 (4 self) - Add to MetaCart
on PCA computation during indexing, we first segment the trajectories into atomic subtrajectories using a curvature zero-crossing based approach followed by clustering of these subtrajectories. A two-level PCA operation with coarse-to-fine retrieval for query trajectory is then performed to generate

Real-time motion trajectory-based indexing and retrieval of video sequences

by Faisal I. Bashir, Student Member, Ashfaq A. Khokhar, Senior Member, Dan Schonfeld, Senior Member - IEEE Trans. Multimedia , 2007
"... Abstract—This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem of trajectory ..."
Abstract - Cited by 22 (2 self) - Add to MetaCart
Abstract—This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem

MM000859.R2 Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences

by Faisal I. Bashir, Student Member, Ashfaq A. Khokhar, Senior Member, Senior Member
"... Abstract—This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem of trajectory ..."
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Abstract—This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem

HMM-based motion recognition system using segmented PCA

by Faisal Bashir, Wei Qu, Ashfaq Khokhar, Dan Schonfeld - Proc. IEEE International Conference on Image Processing (ICIP 2005
"... In this paper, we propose a novel technique for model-based recognition of complex object motion trajectories using Hidden Markov Models (HMM). We build our models on Principal Component Analysis (PCA)-based representation of trajectories after segmenting them into small units of perceptually simila ..."
Abstract - Cited by 20 (5 self) - Add to MetaCart
similar pieces of motions. These subtrajectories are then grouped using spectral clustering to decide on the number of states for each HMM representing a class of object motion. The hidden states of the HMMs are represented by Gaussian Mixtures (GM’s). This way the HMM topology as well as the parameters

Automatic Object TrajectoryBased Motion Recognition using Gaussian Mixture Models

by Faisal Bashir, Ashfaq Khokhar, Dan Schonfeld - IEEE International Conference on Multimedia and Expo , 2005
"... In this paper, we propose a novel technique for model-based recognition of complex object motion trajectories using Gaussian Mixture Models (GMM). We build our models on Principal Component Analysis (PCA)-based representation of trajectories after segmenting them into small units of perceptually sim ..."
Abstract - Cited by 5 (3 self) - Add to MetaCart
similar pieces of motions. These subtrajectories are then fitted with automatically-learnt mixture of Gaussians to estimate the underlying class probability distribution. Experiments are performed on two data sets; the ASL data set (from UCI’s KDD archives) consists of 207 trajectories depicting signs
Results 1 - 10 of 12