Euclidean path modeling for video surveillance

    Authors

    I. N. Junejo;H. Foroosh

    Comments

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    Abbreviated Journal Title

    Image Vis. Comput.

    Keywords

    path modeling; pedestrian surveillance; metric rectification; camera; auto-calibration; trajectory clustering; route detection; SELF-CALIBRATION; CAMERA CALIBRATION; AUTOCALIBRATION; RECONSTRUCTION; REVOLUTION; SURFACES; Computer Science, Artificial Intelligence; Computer Science, Software; Engineering; Computer Science, Theory & Methods; Engineering, Electrical; & Electronic; Optics

    Abstract

    In this paper, we address the issue of Euclidean path modeling in a single camera for activity monitoring in a multi-camera video surveillance system. The method consists of a path building training phase and a testing phase. During the unsupervised training phase, after auto-calibrating a camera and thereafter metric rectifying the input trajectories, a weighted graph is constructed with trajectories represented by the nodes, and weights determined by a similarity measure. Normalized-cuts are recursively used to partition the graph into prototype paths. Each path, consisting of a partitioned group of trajectories, is represented by a path envelope and an average trajectory. For every prototype path, features such as spatial proximity, motion characteristics, curvature, and absolute world velocity are then recovered directly in the rectified images or by registering to aerial views. During the testing phase, using our simple yet efficient similarity measures for these features, we seek a relation between the trajectories of an incoming sequence and the prototype path models to identify anomalous and unusual behaviors. Real-world pedestrian sequences are used to evaluate the steps, and demonstrate the practicality of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

    Journal Title

    Image and Vision Computing

    Volume

    26

    Issue/Number

    4

    Publication Date

    1-1-2008

    Document Type

    Article

    Language

    English

    First Page

    512

    Last Page

    528

    WOS Identifier

    WOS:000253304100005

    ISSN

    0262-8856

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