Tracking multiple pedestrians from monocular videos in an interacting multiple model framework

Zhengqiang Jiang

    Research output: ThesisDoctoral Thesis

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    Abstract

    [Truncated abstract] Detecting and tracking pedestrians in videos have many important computer vision applications including visual surveillance, people recognition, smart environments and human-machine interaction. An automatic pedestrian tracking system comprises 2 main stages: (1) detecting pedestrians in the initial frame or every frame; (2) maintaining the identities of pedestrians in video sequences. Tracking pedestrians in videos is a challenging problem because of background clutters, lighting variation, and occlusion. A reliable pedestrian tracking system should be capable of maintaining the identities of pedestrians in the presence of partial or even, occasionally, full occlusion in video sequences. The appearance model of a pedestrian is widely used in pedestrian tracking. For the appearance model, I compute the colour histograms for the upper and lower bodies of each pedestrian detected by the Histogram of Oriented Gradient (HOG) human detector. Each of these colour histograms is a 3-dimensional entity in the L*a*b colour space and the concatenation of them yields a 4-dimensional tensor. This allows spatial information to be incorporated into the appearance model while the computation cost is kept to its minimum. To get a better estimate of the colour histogram, the kernel density estimation is used to smooth the histogram of the appearance of each pedestrian. The Hellinger distance is used for histogram matching. In this thesis, a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting Multiple Model (IMM) framework is presented. This tracking method involves multiple IMM trackers running in parallel which are tied together by a robust data association component.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Publication statusUnpublished - 2013

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    Color
    Computer vision
    Tensors
    Lighting
    Cameras
    Detectors
    Costs

    Cite this

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    title = "Tracking multiple pedestrians from monocular videos in an interacting multiple model framework",
    abstract = "[Truncated abstract] Detecting and tracking pedestrians in videos have many important computer vision applications including visual surveillance, people recognition, smart environments and human-machine interaction. An automatic pedestrian tracking system comprises 2 main stages: (1) detecting pedestrians in the initial frame or every frame; (2) maintaining the identities of pedestrians in video sequences. Tracking pedestrians in videos is a challenging problem because of background clutters, lighting variation, and occlusion. A reliable pedestrian tracking system should be capable of maintaining the identities of pedestrians in the presence of partial or even, occasionally, full occlusion in video sequences. The appearance model of a pedestrian is widely used in pedestrian tracking. For the appearance model, I compute the colour histograms for the upper and lower bodies of each pedestrian detected by the Histogram of Oriented Gradient (HOG) human detector. Each of these colour histograms is a 3-dimensional entity in the L*a*b colour space and the concatenation of them yields a 4-dimensional tensor. This allows spatial information to be incorporated into the appearance model while the computation cost is kept to its minimum. To get a better estimate of the colour histogram, the kernel density estimation is used to smooth the histogram of the appearance of each pedestrian. The Hellinger distance is used for histogram matching. In this thesis, a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting Multiple Model (IMM) framework is presented. This tracking method involves multiple IMM trackers running in parallel which are tied together by a robust data association component.",
    keywords = "Pedestrian detection, Pedestrian tracking, Occlusion handling, Temporal persistency, Munkres' algorithm, Data association",
    author = "Zhengqiang Jiang",
    year = "2013",
    language = "English",

    }

    Tracking multiple pedestrians from monocular videos in an interacting multiple model framework. / Jiang, Zhengqiang.

    2013.

    Research output: ThesisDoctoral Thesis

    TY - THES

    T1 - Tracking multiple pedestrians from monocular videos in an interacting multiple model framework

    AU - Jiang, Zhengqiang

    PY - 2013

    Y1 - 2013

    N2 - [Truncated abstract] Detecting and tracking pedestrians in videos have many important computer vision applications including visual surveillance, people recognition, smart environments and human-machine interaction. An automatic pedestrian tracking system comprises 2 main stages: (1) detecting pedestrians in the initial frame or every frame; (2) maintaining the identities of pedestrians in video sequences. Tracking pedestrians in videos is a challenging problem because of background clutters, lighting variation, and occlusion. A reliable pedestrian tracking system should be capable of maintaining the identities of pedestrians in the presence of partial or even, occasionally, full occlusion in video sequences. The appearance model of a pedestrian is widely used in pedestrian tracking. For the appearance model, I compute the colour histograms for the upper and lower bodies of each pedestrian detected by the Histogram of Oriented Gradient (HOG) human detector. Each of these colour histograms is a 3-dimensional entity in the L*a*b colour space and the concatenation of them yields a 4-dimensional tensor. This allows spatial information to be incorporated into the appearance model while the computation cost is kept to its minimum. To get a better estimate of the colour histogram, the kernel density estimation is used to smooth the histogram of the appearance of each pedestrian. The Hellinger distance is used for histogram matching. In this thesis, a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting Multiple Model (IMM) framework is presented. This tracking method involves multiple IMM trackers running in parallel which are tied together by a robust data association component.

    AB - [Truncated abstract] Detecting and tracking pedestrians in videos have many important computer vision applications including visual surveillance, people recognition, smart environments and human-machine interaction. An automatic pedestrian tracking system comprises 2 main stages: (1) detecting pedestrians in the initial frame or every frame; (2) maintaining the identities of pedestrians in video sequences. Tracking pedestrians in videos is a challenging problem because of background clutters, lighting variation, and occlusion. A reliable pedestrian tracking system should be capable of maintaining the identities of pedestrians in the presence of partial or even, occasionally, full occlusion in video sequences. The appearance model of a pedestrian is widely used in pedestrian tracking. For the appearance model, I compute the colour histograms for the upper and lower bodies of each pedestrian detected by the Histogram of Oriented Gradient (HOG) human detector. Each of these colour histograms is a 3-dimensional entity in the L*a*b colour space and the concatenation of them yields a 4-dimensional tensor. This allows spatial information to be incorporated into the appearance model while the computation cost is kept to its minimum. To get a better estimate of the colour histogram, the kernel density estimation is used to smooth the histogram of the appearance of each pedestrian. The Hellinger distance is used for histogram matching. In this thesis, a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting Multiple Model (IMM) framework is presented. This tracking method involves multiple IMM trackers running in parallel which are tied together by a robust data association component.

    KW - Pedestrian detection

    KW - Pedestrian tracking

    KW - Occlusion handling

    KW - Temporal persistency

    KW - Munkres' algorithm

    KW - Data association

    M3 - Doctoral Thesis

    ER -