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 F-measure, 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.
['Brandyn White', 'Mubarak Shah']
Automatically Tuning Background Subtraction Parameters using Particle Swarm Optimization