Shape-based template matching for time series data

Niennattrakul, Vit; Srisai, Dararat; Ratanamahatana, Chotirat Ann

doi:10.1016/j.knosys.2011.04.015

Cited by 40 publications

(26 citation statements)

References 25 publications

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“…So, the homographic transformation estimation can be performed using shape matching between these characters. Most of the methods proposed to match shapes are based on dynamic time warping algorithm [20], [21], [24]. These algorithms search through all probable matching answers and select the best one.…”

Section: Goal Oriented Homographic Transformation Estimationmentioning

confidence: 99%

Precise Vehicle Speed Measurement Based on a Hierarchical Homographic Transform Estimation for Law Enforcement Applications

Eslami

Raie

Faez

2016

IEICE Trans. Inf. & Syst.

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SUMMARYToday, computer vision is used in different applications for intelligent transportation systems like: traffic surveillance, driver assistance, law enforcement etc. Amongst these applications, we are concentrating on speed measurement for law enforcement. In law enforcement applications, the presence of the license plate in the scene is a presupposition and metric parameters like vehicle's speed are to be estimated with a high degree of precision. The novelty of this paper is to propose a new precise, practical and fast procedure, with hierarchical architecture, to estimate the homraphic transform of the license plate and using this transform to estimate the vehicle's speed. The proposed method uses the RANSAC algorithm to improve the robustness of the estimation. Hence, it is possible to replace the peripheral equipment with vision based systems, or in conjunction with these peripherals, it is possible to improve the accuracy and reliability of the system. Results of experiments on different datasets, with different specifications, show that the proposed method can be used in law enforcement applications to measure the vehicle's speed. key words: feature extraction, RANSAC estimator, license plate, speed measurement, law enforcement

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Section: Goal Oriented Homographic Transformation Estimationmentioning

confidence: 99%

Precise Vehicle Speed Measurement Based on a Hierarchical Homographic Transform Estimation for Law Enforcement Applications

Eslami

Raie

Faez

2016

IEICE Trans. Inf. & Syst.

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“…The concept of template has been introduced to time series to detect specific patterns or shapes [18,19,40,41,48]. Frank et al [18] propose Geometric Template Matching (GeTeM) which uses time-delay embeddings for building models from segments of time series and compares the reconstructed dynamical systems in terms of their state space as well as their dynamics.…”

Section: Related Workmentioning

confidence: 99%

“…In [19], a novel and flexible approach is proposed based on segmental semi-Markov models. In [40,41,48], meaningful templates are constructed with shape-based averaging algorithms, such as Prioritized Shape Averaging (PSA) [40] and Accurate Shape Averaging (ASA) [48]. Wei et al propose the Atomic Wedgie method "that exploits the commonality among the predefined patterns to allow monitoring at higher bandwidths, while maintaining a guarantee of no false dismissals" [56].…”

Section: Related Workmentioning

confidence: 99%

“…When matching a predefined phenomenon (a template [40,41,48]) with the time series under investigation, it is not always required to involve every individual measurement in the selected interval and in the template. In fact, when a certain level of fuzzy matching is required, it makes sense to somehow simplify the signal, or extract some key features that are characteristic for the sequence in question.…”

Section: Introductionmentioning

confidence: 99%

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Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

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a b s t r a c tPredefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.

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“…This paper presents an effective approach based on DP algorithm and Lagrange multipliers to optimize the objective function. When calculating fuzzy cluster centers, this paper employs cubicspline DTW (CDTW) averaging function (Niennattrakul et al 2012). In addition, a DP-based technique is utilized to reduce the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Dynamic programming-based optimization for segmentation and clustering of hydrometeorological time series

Guo

Liu

2015

Stoch Environ Res Risk Assess

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In this study, we propose a new segmentation algorithm to partition univariate and multivariate time series, where fuzzy clustering is realized for the segments formed in this way. The clustering algorithm involves a new objective function, which incorporates an extra variable related to segmentation, while dynamic time warping (DTW) is applied to determine distances between nonequal-length series. As optimizing the introduced objective function is a challenging task, we put forward an effective approach using dynamic programming (DP) algorithm. When calculating the DTW distance, a DP-based method is developed to reduce the computational complexity. In a series of experiments, both synthetic and real-world time series are used to evaluate the performance of the proposed algorithm. The results demonstrate higher effectiveness and advantages of the constructed algorithm when compared with the existing segmentation approaches.

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Shape-based template matching for time series data

Cited by 40 publications

References 25 publications

Precise Vehicle Speed Measurement Based on a Hierarchical Homographic Transform Estimation for Law Enforcement Applications

Precise Vehicle Speed Measurement Based on a Hierarchical Homographic Transform Estimation for Law Enforcement Applications

Predefined pattern detection in large time series

Dynamic programming-based optimization for segmentation and clustering of hydrometeorological time series

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