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Research Article Open access

A Genetic Algorithm for Optimizing Background Subtraction Parameters in Computer Vision

Ramesh Rajagopalan

Current Journal of Applied Science and Technology · pp. 4148–4155 · Published 7 Aug 2014

10.9734/BJAST/2014/12093

Abstract

Tracking moving objects in a video sequence is a critical task in several computer vision applications. A common approach is to perform background subtraction which identifies moving objects in a video frame. The mixture of Gaussians model is one of the most popular techniques for performing background subtraction. The performance of the mixture of Gaussian model strongly depends on parameters such as learning rate, background ratio, and number of Gaussians. Fine tuning these parameters is a huge challenge for efficient performance of the background subtraction algorithm. In this work, we propose a genetic algorithm to determine the optimal values of the learning rate and background ratio. Experiments based on the Wallflower test images demonstrate the superior performance of the genetic algorithm when compared to a recently proposed particle swarm optimization approach.  

Mixture of gaussians background subtraction genetic algorithm

References (12)

  1. 1 Genetic Algorithms in Search, Optimization and Machine Learning
  2. 2 Adaptive background mixture models for real-time tracking [DOI]
  3. 3 Pfinder: real-time tracking of the human body [DOI]
  4. 4 An Overview of Evolutionary Algorithms for Parameter Optimization [DOI]
  5. 5 Background subtraction techniques: a review [DOI]
  6. 6 ViBe: A Universal Background Subtraction Algorithm for Video Sequences [DOI]
  7. 7 Uniform crossover in genetic algorithms
  8. 8 Wallflower: principles and practice of background maintenance [DOI]
  9. 9 Towards robust automatic traffic scene analysis in real-time [DOI]
  10. 10 Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey [DOI]
  11. 11 Automatically Tuning Background Subtraction Parameters using Particle Swarm Optimization [DOI]
  12. 12 Realtime background subtraction from dynamic scenes [DOI]

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