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Trajectory based vehicle counting and anomalous event visualization in smart cities

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Trajectory based vehicle counting and anomalous event visualization in smart cities. / Mehboob, Fozia; Abbas, Muhammad; Jiang, Richard et al.
In: Cluster Computing, Vol. 21, No. 1, 01.03.2018, p. 443-452.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Mehboob, F, Abbas, M, Jiang, R, Rauf, A, Khan, S & Rehman, S 2018, 'Trajectory based vehicle counting and anomalous event visualization in smart cities', Cluster Computing, vol. 21, no. 1, pp. 443-452. https://doi.org/10.1007/s10586-017-0885-5

APA

Mehboob, F., Abbas, M., Jiang, R., Rauf, A., Khan, S., & Rehman, S. (2018). Trajectory based vehicle counting and anomalous event visualization in smart cities. Cluster Computing, 21(1), 443-452. https://doi.org/10.1007/s10586-017-0885-5

Vancouver

Mehboob F, Abbas M, Jiang R, Rauf A, Khan S, Rehman S. Trajectory based vehicle counting and anomalous event visualization in smart cities. Cluster Computing. 2018 Mar 1;21(1):443-452. Epub 2017 May 1. doi: 10.1007/s10586-017-0885-5

Author

Mehboob, Fozia ; Abbas, Muhammad ; Jiang, Richard et al. / Trajectory based vehicle counting and anomalous event visualization in smart cities. In: Cluster Computing. 2018 ; Vol. 21, No. 1. pp. 443-452.

Bibtex

@article{c6a873a1fc95421fa5201d93cc15064d,
title = "Trajectory based vehicle counting and anomalous event visualization in smart cities",
abstract = "Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it{\textquoteright}s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.",
author = "Fozia Mehboob and Muhammad Abbas and Richard Jiang and Abdul Rauf and Shoab Khan and Saad Rehman",
year = "2018",
month = mar,
day = "1",
doi = "10.1007/s10586-017-0885-5",
language = "English",
volume = "21",
pages = "443--452",
journal = "Cluster Computing",
number = "1",

}

RIS

TY - JOUR

T1 - Trajectory based vehicle counting and anomalous event visualization in smart cities

AU - Mehboob, Fozia

AU - Abbas, Muhammad

AU - Jiang, Richard

AU - Rauf, Abdul

AU - Khan, Shoab

AU - Rehman, Saad

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.

AB - Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.

U2 - 10.1007/s10586-017-0885-5

DO - 10.1007/s10586-017-0885-5

M3 - Journal article

VL - 21

SP - 443

EP - 452

JO - Cluster Computing

JF - Cluster Computing

IS - 1

ER -