A Genetic Algorithm for Optimizing Background Subtraction Parameters in Computer Vision
Current Journal of Applied Science and Technology · pp. 4148–4155 · Published 7 Aug 2014
10.9734/BJAST/2014/12093Abstract
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.
Cited by 2
Sujoy Madhab Roy, Ashish Ghosh · Expert Systems with Applications · 2019
Avinash Ratre, Vinod Pankajakshan · The Imaging Science Journal · 2017
Related research
- An Intelligent Tuned Harmony Search Algorithm for Optimum Design of Steel Framed Structures to AISC-LRFD — shares topic coverage
- A Genetic Algorithm with Neighborhood Search to Solve Integer and Linear Programming Problems — shares topic coverage
- An Optimized Genetic Approach for Scheduling Task Duplication in Parallel Systems — shares topic coverage
- Genetic Algorithm Based on K-means-Clustering Technique for Multi-objective Resource Allocation Problems — shares topic coverage
- A Genetic Algorithm in Green Cloud Computing — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
2
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.