A Cost Effective Method for Matching the 3D Motion Trajectories

Pham, Hai-Trieu; Kim, Jung-Ja; Won, Yonggwan

doi:10.1007/978-94-007-5860-5_107

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Two trajectories are compared with a modified continuous dynamic time warping algorithm. A similar idea, but with the addition of elbow points to be the feature for representing trajectories was later proposed in [22], and for trajectory comparison, the aforementioned LCSS was adopted.…”

Section: Related Workmentioning

confidence: 99%

Video Object Retrieval by Trajectory and Appearance

Lai

Yang

2015

IEEE Trans. Circuits Syst. Video Technol.

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The prevalence of video recording capability, either on surveillance systems or mobile devices, has contributed to the popularity of video data. As a result, video management has become relatively more important than before and, in particular, video retrieval has been one of the main issues in this regard. Traditional video retrieval systems take texts as the inputs to look for similar information from the title, annotation or embedded textual data of a video, in a way that is very similar to the keyword search adopted by a common search engine. However, the lack of visual information specification during a search often makes the result rather inaccurate or even useless. For this reason, video retrieval systems using images or videos as the inputs have also been proposed; nevertheless, the associated ambiguity and complexity have made the implementation of such systems relatively difficult and, therefore, those systems are not as successful as desired. To address this, in this paper, we propose to perform a video retrieval of a desired object through the inputs of its trajectory and/or appearance, together with the help of a 3-D graphical user interface for more intuitive interactions, so that more satisfactory results can be achieved. We firmly believe that such a framework could serve as the foundation for behavior analysis used in many surveillance systems.Index Terms-Object reidentification, object tracking, video retrieval. 1051-8215

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Section: Related Workmentioning

confidence: 99%

Video Object Retrieval by Trajectory and Appearance

Lai

Yang

2015

IEEE Trans. Circuits Syst. Video Technol.

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“…If there is no shift, the simple rigid 3D registration problem (5) is recovered. Remark that methods based on rotation invariant shape signatures/features (as curvature or torsion for example) [25,26,27] or rotation invariant metrics [28] are not considered here since they do not make the estimation of the rotation matrix. Moreover, they match a 3D curve with an other, and not a 3D curve with a linear combination of 3D patterns.…”

Section: D Matchingmentioning

confidence: 99%

Decomposition and dictionary learning for 3D trajectories

2014

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A new model for describing a three-dimensional (3D) trajectory is proposed in this article. The studied trajectory is viewed as a linear combination of rotatable 3D patterns. The resulting model is thus 3D rotation invariant (3DRI). Moreover, the temporal patterns are considered as shift-invariant. This article is divided into two parts based on this model. On the one hand, the 3DRI decomposition estimates the active patterns, their coefficients, their rotations and their shift parameters. Based on sparse approximation, this is carried out by two non-convex optimizations: 3DRI matching pursuit (3DRI-MP) and 3DRI orthogonal matching pursuit (3DRI-OMP). On the other hand, a 3DRI learning method learns the characteristic patterns of a database through a 3DRI dictionary learning algorithm (3DRI-DLA). The proposed algorithms are first applied to simulation data to evaluate their performances and to compare them to other algorithms. Then, they are applied to real motion data of cued speech, to learn the 3D trajectory patterns characteristic of this gestural language.

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“…So, despite the lack of a guaranteed experimenting conclusion, relying on an extensive analysis [4], a comparison between developed algorithm and the classical DTW is considered sufficient. From Hidden Markov Models [5] and 'elbow' reductions [6] to kernel-based representations [7], there are various ways of handling trajectory analysis tasks but none of these are covariance-based except [8], where covariance matrices are instrumented to develop DTW rather than extracting explicit vector set descriptions. So, in terms of feature formation, that is, deduction of fixed-dimensional vectors from ordered vector sets, literature is still lacking in covariance-based analysis; especially when it comes to shrunk models.…”

Section: Introductionmentioning

confidence: 99%

Covariance Features for Trajectory Analysis

Karadeniz

Maras

2018

ElAEE

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In this work, it is demonstrated that covariance estimator methods can be used for trajectory classification. It is shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. Compared to Dynamic Time Warping, application of explained technique is faster and yields more accurate results. An improvement of Dynamic Time Warping based on counting statistical comparison of base distance measures is also achieved. Results on Australian Sign Language and Character Trajectories datasets are reported. Experiment realizations imply feasibility through covariance attributes on time series.

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A Cost Effective Method for Matching the 3D Motion Trajectories

Cited by 5 publications

References 6 publications

Video Object Retrieval by Trajectory and Appearance

Video Object Retrieval by Trajectory and Appearance

Decomposition and dictionary learning for 3D trajectories

Covariance Features for Trajectory Analysis

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