Evaluation of the background modeling method Auto-Adaptive Parallel Neural Network Architecture in the SBMnet dataset
Mario Ignacio Chacon-Murguia, Juan A. Ramírez-Quintana, Graciela María de Jesús Ramírez-Alonso · 2016
This paper describes the evaluation of the Auto-Adaptive Parallel Neural Network Architecture, AAPNNA, in the SBMnet dataset. AAPNNA is an artificial neural model based on two networks whose neurons represent two different Background models that adapt their parameters at different rates. A very important feature of AAPNNA is its capacity to auto adapt to new scenario conditions as demonstrated with the results of the Illumination Change category. AAPNNA presented good qualitative results in several of the videos available for comparison. The overall quantitative performance with the complete SBMC 2016 dataset can be considered as an acceptable performance with the evaluation of all the videos and the performance interval of each metric.