A New Rotation Invariant Similarity Measure for Trajectories

Fashandi, Homa; Moghaddam, Amir Masoud Eftekhari

doi:10.1109/cira.2005.1554347

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…The authors of [47] presented a similarity measurement between trajectories based on Pearson's correlation coefficient and the coefficient of determination. In [48], the authors proposed a similarity measure for trajectories based on longest common subsequence (LCS) method and tested it on a database consisting of tracks of moving vehicles in cities. Reference [49] presents a robust-to-noise version of the LCS method.…”

Section: Trajectory Analysismentioning

confidence: 99%

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk¹,

Komorkiewicz²,

Skruch

et al. 2022

IEEE Access

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T HE paper proposes a formal approach for describing and evaluating the datasets that are used in automotive applications for machine learning, testing, and validation purposes. Proper, that is, qualitative and quantitative characterization of the datasets can simplify the analysis, evaluation, and comparison of perception-based algorithms designed for highly automated vehicles. Such formalism is also needed to achieve compliance with the automotive industry safety standards that have been recently introduced. Characterization in the form of size or type of raw data, number of recognized and classified objects, and environmental parameters is not perfectly suitable for describing both the static and dynamic aspects of automotive datasets; therefore, another approach is required. In this paper, an efficient method based on an object tracking mechanism, grid representation of the sensor field of view, heatmap concept, and Wasserstein metric is proposed. The efficiency of the method is demonstrated by its ability to handle both the size, properties, and diversity of the dataset, including static and time-varying aspects. The presented description can also be used to compare different datasets and to define the amount of data to be collected.

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Section: Trajectory Analysismentioning

confidence: 99%

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk¹,

Komorkiewicz²,

Skruch

et al. 2022

IEEE Access

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“…Methods like conjugate gradients and steepest descent are used to set the hyperparameters to maximize the marginal likelihood in (6).…”

Section: Gaussian Processesmentioning

confidence: 99%

“…In [6], a rotation-invariant similarity measure for trajectories is proposed. This method is based on the longest common subsequence(LCS) for trajectory clustering.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameters of Gaussian process as features for trajectory classification

Haranadh

Sekhar

2008

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

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In this paper, we address the trajectory classification problem in Gaussian process framework without using Gaussian process based classification directly. Properties of the function corresponding to a trajectory are captured into the hyperparameters of a Gaussian process. As different trajectories have different properties, hyperparameters are different for these trajectories. In the hyperparametric space, different clusters are formed for noisy, shifted versions of the trajectories. The hyperparameters are used as features representing a trajectory and the classification task is performed in the hyperparametric space. Classification performance of the proposed method is evaluated on simulated data and also on realworld time series data.

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“…In [13], Fashandi et al consider a rotation invariant representation using relative sequences of angles. In combination with some features defined above, some recent relevant works deal with the color density (RGB information) [25] [26] and the sizes of the tracked objects [28] [27].…”

Section: A Trajectory Feature Representationmentioning

confidence: 99%

A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories

Hervieu

Bouthémy

Cadre

2008

IEEE Trans. Circuits Syst. Video Technol.

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I. INTRODUCTIONThis work is motivated by the problem of video motion class detection in order to understand object behaviors. The underlying issue is the content-based exploitation of video footage which is of continuously increasing interest in numerous applications, e.g., for retrieving video sequences in large TV archives [12] [42], creating automatic video summarization of sport TV programs [32], or detecting specific actions or activities in video-surveillance [6] [26]. It implies to shorten the well-known semantic gap between computed low-level features and high-level concepts. Considering 2D trajectories is attractive since they form computable image features which capture elaborated spatiotemporal information on the viewed actions. Methods for tracking moving objects in an image sequence are now available to get reliable enough 2D trajectories in various situations. These trajectories are given as a set of consecutive positions in the image plane (x, y) over time. If they are embedded in an appropriate modeling framework, highlevel information on the dynamic scene can then be reachable.

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A New Rotation Invariant Similarity Measure for Trajectories

Cited by 8 publications

References 3 publications

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Hyperparameters of Gaussian process as features for trajectory classification

A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories

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