Title

Automatically Tuning Background Subtraction Parameters Using Particle Swarm Optimization

Abstract

A common trait of background subtraction algorithms is that they have learning rates, thresholds, and initial values that are hand-tuned for a scenario in order to produce the desired subtraction result; however, the need to tune these parameters makes it difficult to use state-of-the-art methods, fuse multiple methods, and choose an algorithm based on the current application as it requires the end-user to become proficient in tuning a new parameter set. The proposed solution is to automate this task by using a Particle Swarm Optimization (PSO) algorithm to maximize a fitness function compared to provided ground-truth images. The fitness function used is the Fmeasure, which is the harmonic mean of recall and precision. This method reduces the total pixel error of the Mixture of Gaussians background subtraction algorithm by more than 50% on the diverse Wallflower data-set. ©2007 IEEE.

Publication Date

1-1-2007

Publication Title

Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Number of Pages

1826-1829

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/icme.2007.4285028

Socpus ID

46449114701 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/46449114701

This document is currently not available here.

Share

COinS