Automatically Tuning Background Subtraction Parameters using Particle Swarm Optimization

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 stateof-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.

• Type: Conference
• Year: 2007
• Where: ICMCS
• Authors: Brandyn White, Mubarak Shah
• Keywords: Background Subtraction Algorithm, Gaussians Background Subtraction, ICMCS 2007, fitness Function