A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification

In this paper, many vision-based classification techniques were presented relying only on a digital camera without the need for any extra hardware components. Vehicle-type classification is considered a core module for many intelligent transportation applications, such as speed monitoring, smart parking systems, and traffic analysis.  

In this paper, we present a comprehensive study of the effect of these two characteristics on the vehicle classification process in terms of accuracy and performance. We apply a set of different state-of-the-art image classifiers to the BIT-Vehicle and Label Me data sets. 

Besides, we examine the effect of color by converting each color version to a gray-scale one. Experimental results show that there is no significant influence of both color and spatial resolutions of the vehicle images on the classification results obtained by most state-of-the-art image classification methods.