Overview and Benchmarking of Motion Detection Methods

• Published in: Background Modeling and Foreground Detection for Video Surveillance pp. 575 - 600
• Format: Book Chapter
• Language: English
• 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
• DOI: 10.1201/b17223-34

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.