Nonlinear Normalization Model to Improve the Performance of Neural Networks
Daehyon Kim · Asia-pacific Journal of Convergent Research Interchange · 2020
An intelligent transportation system (ITS) generally contains an automatic traffic video-surveillance system as a primary subsystem.Such subsystem incorporates the capabilities of neural networks for the efficient and effective recognition and classification of complicated spatial and temporal patterns in real-world traffic scenarios.Notably, the properties of input vectors are the key factors in determining the performance of neural networks.These properties are governed by the method used to normalize these vectors; a simple linear scaling model is widely employed for normalizing input vectors.This study proposes the use of a nonlinear normalization model for input vector normalization.The proposed technique is subsequently applied to neural networks to resolve classification problems encountered when analyzing real-world traffic image data.The experimental results show that the proposed model can produce higher prediction accuracy, when compared to the existing linear-based approach models.This model has the potential to improve the performance in traffic machine vision applications.