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Direction-Oriented Topic Modeling with Applications in Traffic Scene Analysis

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International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Unlike text analysis for which topic models are historically developed, traffic video analysis is dealing with much simpler topics, made of restricted motion patterns. In this paper, we propose a dual-layer direction-oriented framework for more efficient traffic motion patterns description based on topic models through considering the simplicity of traffic topics. The aforesaid framework compels the involved topic models to learn the foreknown visually meaningful motion patterns that exist in traffic scenes, as developed theoretically in this paper. Experimental results produced by common datasets show that the proposed method provides more intuitive topics for traffic flow description. Based on experimental results, our framework outperforms other topic-model based methods by 4% to more than 11% in detecting abnormal events, in terms of the area under the Receiver Operating Characteristic curve. In addition to that, in a scene analysis evaluation at intersections equipped with traffic signals, our method reaches 4% higher traffic phase detection accuracy, compared to conventional topic models.

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Data Availability

Dataset is Publically available at: http://www.eecs.qmul.ac.uk/~sgg/QMUL_Junction_Datasets/Junction/Junction.html.

Notes

  1. http://lear.inrialpes.fr/~verbeek/code/plsa.tar.gz.

  2. http://www.cs.columbia.edu/~blei/lda-c/.

  3. http://bigml.cs.tsinghua.edu.cn/~jun/stc.shtml.

  4. https://www.jaist.ac.jp/~s1060203/codes/fstm/.

  5. Publically available at http://www.eecs.qmul.ac.uk/~sgg/QMUL_Junction_Datasets/Junction/Junction.html.

  6. Publically available at http://www.eecs.qmul.ac.uk/~sgg/QMUL_Junction_Datasets/Roundabout/Roundabout.html.

Abbreviations

AUROC :

Area under ROC

DPMM :

Dirichlet Process Mixture Model

FPR :

False Positive Rate

FSTM :

Fully Sparse Topic Model

GSTC :

Group Sparse Topical Coding

HDP :

Hierarchical Dirichlet Process

LDA :

Latent Dirichlet Allocation

PCA :

Principal component analysis

PLSA :

Probabilistic Latent Semantic Analysis

ROC :

Receiver Operating Characteristic

SRC :

sparse reconstruction cost

STC :

Sparse Topical Coding

TPR :

True Positive Rate

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Funding

This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors.

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Authors and Affiliations

  1. IT Research Faculty, ICT Research Institute, Tehran, Iran

    Parvin Ahmadi

  2. Electronics Research Institute, Sharif University of Technology, Tehran, Iran

    Iman Gholampour

Authors
  1. Parvin Ahmadi
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  2. Iman Gholampour
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Contributions

PA: Study design, reviewing the literature, proposing the method, interpretation of the results, drafting the manuscript.

IGH: Study design, interpretation of the results, statistical analysis, revision of the manuscript.

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Correspondence to Parvin Ahmadi.

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Ahmadi, P., Gholampour, I. Direction-Oriented Topic Modeling with Applications in Traffic Scene Analysis. Int. J. ITS Res. 22, 18–33 (2024). https://doi.org/10.1007/s13177-023-00373-1

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