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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    A new pattern recognition method for abnormal event detection in crowded scenarios

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    13101098_CSE.pdf (1.604Mb)
    Date
    2017-04-18
    Publisher
    BRAC University
    Author
    Mostafa, Tahjid Ashfaque
    Metadata
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    URI
    http://hdl.handle.net/10361/8241
    Abstract
    We propose an autonomous video surveillance system which analyzes surveillance footages of extremely crowded scenes and detects abnormal events. For any particular scenario, any event that diverts from the usual pattern can be classified as an abnormal event. The model analyzes the local spatial-temporal motion pattern and detects abnormal motion variations and sudden changes. It can be divided into two major parts, selecting a set of Points of Interest (POI) from given frames using ORB (Oriented FAST and Rotated BRIEF) feature detector and tracking them across multiple frames and dividing the input video frame in a number of cubes and track the motion patterns in each of the cubes for spatial-temporal statistical deviations. To evaluate the performance of proposed model we utilize several datasets and compare the acquired results of the proposed model with various state-of-the art models. Experimental results demonstrate that the proposed model outperforms the other models by exhibiting an average of 96.12% accuracy using Convolutional Neural Network.
    Keywords
    Image processing; Pattern recognition
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 31-34).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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