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84
Discovering similar multidimensional trajectories
- In ICDE
, 2002
"... We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest C ..."
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Cited by 260 (6 self)
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We investigate techniques for analysis and retrieval of object trajectories in a two or three dimensional space. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach. 1
Robust and fast similarity search for moving object trajectories
- In Proc. ACM SIGMOD Int. Conf. on Management of Data
, 2005
"... An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The ex-isting distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, dis ..."
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Cited by 155 (14 self)
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An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The ex-isting distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imper-fections. Analysis and comparison of EDR with other popular dis-tance functions, such as Euclidean distance, Dynamic Time Warp-ing (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning tech-niques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increas-ing the pruning power significantly. The experimental results con-firm the superior efficiency of the combined methods. 1.
Derivative dynamic time warping
- In SIAM International Conference on Data Mining
, 2001
"... Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two se ..."
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Cited by 121 (1 self)
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two sequences have the approximately the same overall
Pattern Extraction for Time Series Classification
, 2001
"... In this paper, we propose some new tools to allow machine learning classifiers to cope with time series data. We first argue that many time-series classification problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to find patte ..."
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Cited by 72 (2 self)
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In this paper, we propose some new tools to allow machine learning classifiers to cope with time series data. We first argue that many time-series classification problems can be solved by detecting and combining local properties or patterns in time series. Then, a technique is proposed to find patterns which are useful for classification. These patterns are combined to build interpretable classification rules. Experiments, carried out on several artificial and real problems, highlight the interest of the approach both in terms of interpretability and accuracy of the induced classifiers.
Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes
- in ICPR ’06: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06
"... This paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. Six similarity measures are presented and the performance is evaluated by Correct Clustering Rate (CCR) and Time Cost (TC). The experimental results demonstrate that in outdoor surveillan ..."
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Cited by 47 (0 self)
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This paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. Six similarity measures are presented and the performance is evaluated by Correct Clustering Rate (CCR) and Time Cost (TC). The experimental results demonstrate that in outdoor surveillance scenes, the simpler PCA+Euclidean distance is competent for the clustering task even in case of noise, as more complex similarity measures such as DTW, LCSS are not efficient due to their high computational cost.
Iterative deepening dynamic time warping for time series
- In Proc 2 nd SIAM International Conference on Data Mining
, 2002
"... Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time s ..."
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Cited by 39 (8 self)
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time series [7]. Debregeas and Hebrail [8]
Learning trajectory patterns by clustering: Experimental studies and comparative evaluation
- In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
, 2009
"... Abstract Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates ..."
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Cited by 35 (7 self)
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Abstract Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.
Counting Pedestrians in Video Sequences Using Trajectory Clustering
- IEEE Transactions on Circuits and Systems for Video Technology, Vol.16, Issue
, 2006
"... Abstract—In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in o ..."
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Cited by 34 (1 self)
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Abstract—In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in order to reduce the bias between the number of tracks and the real number of targets. The main hypothesis is that those trajectories belonging to the same human body are more similar than trajectories belonging to different individuals. Several data representations and different distance/similarity measures are proposed and compared, under a common hierarchical clustering framework, and both quantitative and qualitative results are presented. I.
Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion
, 2013
"... Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variab ..."
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Cited by 31 (2 self)
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Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as a temporal clustering one, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for the time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.
A Brief Survey on Sequence Classification
"... Sequence classification has a broad range of applications such as genomic analysis, information retrieval, health informatics, finance, and abnormal detection. Different from the classification task on feature vectors, sequences do not have explicit features. Even with sophisticated feature selectio ..."
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Cited by 31 (1 self)
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Sequence classification has a broad range of applications such as genomic analysis, information retrieval, health informatics, finance, and abnormal detection. Different from the classification task on feature vectors, sequences do not have explicit features. Even with sophisticated feature selection techniques, the dimensionality of potential features may still be very high and the sequential nature of features is difficult to capture. This makes sequence classification a more challenging task than classification on feature vectors. In this paper, we present a brief review of the existing work on sequence classification. We summarize the sequence classification in terms of methodologies and application domains. We also provide a review on several extensions of the sequence classification problem, such as early classification on sequences and semi-supervised learning on sequences. 1.
