Overview and Benchmarking of Motion Detection Methods

Motion detection is closely coupled with higher level inference tasks such as detection, localization, tracking, and classification of moving objects, and is often considered to be a preprocessing step. Its importance can be gauged by the large number of algorithms that have been developed to-date an...

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Bibliographic Details
Published inBackground Modeling and Foreground Detection for Video Surveillance pp. 575 - 600
Format Book Chapter
LanguageEnglish
Published Chapman and Hall/CRC 2015
Subjects
Combination Rule
Motion Detection Methods
Normalize RGB
Background Model
IEEE Xplore
Motion Detection
Camera Jitter
Chebyshev Inequality
Background Pixel
Updating Scheme
Generalized Gaussian Model
Motion Detection Techniques
Post-processing Methods
Motion History Image
Multi-criteria Ranking
Non-Parametric Kernel Density Estimation
Motion Segmentation
RGB Color
Segmentation Map
Background Motion
Dirichlet Process
Background Subtraction
Foreground Pixels
Background Subtraction Method
GMM Method
Online AccessGet full text
DOI10.1201/b17223-34

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Summary:Motion detection is closely coupled with higher level inference tasks such as detection, localization, tracking, and classification of moving objects, and is often considered to be a preprocessing step. Its importance can be gauged by the large number of algorithms that have been developed to-date and the even larger number of articles that have been published on this topic. A quick search for ”motion detection” on IEEE Xplore c© returns over 20,000 papers. This shows that motion detection is a fundamental topic for a wide range of video analytic applications. It also shows that the number of motion detection methods proposed so far is impressively large.
DOI:10.1201/b17223-34