Comparison of Target Detection Algorithms using Adaptive Background Models
Daniela Hall, Sabrina Pesnel, Rémi Emonet, James L. Crowley, Jacinto C. Nascimento, Pedro Ribeiro, Plínio Moreno, José Santos-Victor, E. Andrade, Thomas List, R.B. Fisher · 2006
This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.