Unsupervised trajectory pattern classification using hierarchical Dirichlet Process Mixture hidden Markov model

Bastani, Vahid; Regazzoni, Carlo S.

doi:10.1109/mlsp.2014.6958916

Cited by 18 publications

(4 citation statements)

References 11 publications

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“…There are studies on supervised approaches such as [2], [6], [9], [19], [23], [26], [27] that are based on labeled dataset. Unsupervised approaches such as [1], [14], [24], [25], [28] use unlabeled dataset to cluster similar trajectories and use clustered data to train models for classification. Tracking [8], [13], [18], [35] is also an important task to build a complete traffic analysis framework.…”

Section: A Related Workmentioning

confidence: 99%

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Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos

Kumaran

Dogra

Roy

2019

IEEE Trans. Intell. Transport. Syst.

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Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling based heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC) has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to associate pixels belonging to similar cluster in subsequent frames. The algorithm is fast and produces accurate results in Θ(kn) time, where k is the number of clusters and n the number of pixels. Our experimental validation with publicly available datasets reveals that the proposed framework has good potential to open-up new opportunities for real-time traffic analysis.

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Section: A Related Workmentioning

confidence: 99%

“…Distance Dependent Chinese Restaurant Process (DDCRP) [4] underlines the usage of distance in the inference process for faster convergence. In DPMM, the number of clusters formed on a given data depends on the concentration parameter (α) of the model described in (1)(2)(3)(4).…”

Section: B Motivation Of the Researchmentioning

confidence: 99%

Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos

Kumaran

Dogra

Roy

2019

IEEE Trans. Intell. Transport. Syst.

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“…As a result, all the methods that have previously been developed have one or more of the above disadvantages. Some of the essential reasons for restricting the practical applications of the relevant results presented in many previous works [2,8,10,16,[19][20][21], especially in cases of analyzing flight data, are as follows:…”

Section: Introductionmentioning

confidence: 99%

Optimizing the mutual arrangement of pilot indicators on an aircraft dashboard and analysis of this procedure from the viewpoint of quantum representations

Kuravsky¹,

Greshnikov²

2021

J Appl Eng Science

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The purpose of this work is to present the first attempt to provide quantitative analysis and objective justification for designers’ decisions that relate to the arrangement of pilot indicators on an aircraft dashboard with the use of video oculography measurements. To date, such decisions have been made only based on the practical experience accumulated by designers and subjective expert assessments. A new method for optimizing the mutual arrangement of the dashboard indicators is under consideration. This is based on iterative correction of the gaze transition probability matrix between the selected zones of attention, to minimize the difference between the stationary distribution of relative frequencies of gaze that are staying in these zones and the corresponding desirable target eye movements that are given for distribution for qualified pilots. When solving the subsequent multidimensional scaling problem, the gaze transition probability matrix that is obtained is considered to be the similarity matrix, the elements of which quantitatively characterize the proximity between the zones of attention. The main findings of this novel work are as follows: the use of oculography data to justify dashboard design decisions, the optimizing method itself, and its mathematical components, as well as analysis of the optimization in question from the viewpoint of quantum representations, all revealed design mistakes. The results that were obtained can be applied for prototyping variants of aircraft dashboards by rearranging the display areas associated with the corresponding zones of attention.

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“…Trajectory beams are corresponding to the groups of 3D-trajectories with similar characteristics. For partitioning of trajectory sample into the beams such methods as PCA [3], nonparametric approach based on Dynamic Bayesian Networks [4] and spectral clustering [5] are used. The methods are based on reduction of analyzed data space dimensions.…”

Section: Introductionmentioning

confidence: 99%

New data mining technique for multidimensional aircraft trajectories analysis

Solntseva-Chaley¹

2016

ITM Web Conf.

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Abstract. Under conditions of growing airport workload, airspace sectorization is necessary for accidents prevention. Sectorization should be performed taking into account regular traffic of aircrafts. A new data mining technique, solving the problem, is described. It allows to fulfill stable partition of aircraft intent trajectory sample into the groups (asymptotically converged beams), corresponding to the same runway approaches. Method is taking into account special geometric characteristics (curvature, torsion and multiple intersections) of multidimensional space trajectories of aircrafts.

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Unsupervised trajectory pattern classification using hierarchical Dirichlet Process Mixture hidden Markov model

Cited by 18 publications

References 11 publications

Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos

Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos

Optimizing the mutual arrangement of pilot indicators on an aircraft dashboard and analysis of this procedure from the viewpoint of quantum representations

New data mining technique for multidimensional aircraft trajectories analysis

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