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      • HARVEST
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      Detection and segmentation of moving objects in video using optical vector flow estimation

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      RishabhMScThesis.pdf (16.34Mb)
      Date
      2008
      Author
      Malhotra, Rishabh
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.
      Degree
      Master of Science (M.Sc.)
      Department
      Electrical Engineering
      Program
      Electrical Engineering
      Supervisor
      Takaya, Kunio
      Committee
      Teng, Hsiang-Yung (Daniel); Fotouhi, Reza; Faried, Sherif O.; Wahid, Khan
      Copyright Date
      2008
      URI
      http://hdl.handle.net/10388/etd-07222008-110351
      Subject
      Motion Segmentation
      Moving Objects
      Optical Vector Flow Estimation
      Motion Detection
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      • Graduate Theses and Dissertations
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