A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification
Aim: To provide an answer for the question: what are the best values for these two properties (spatial resolution and color) of digital cameras to use in vision-based vehicle classification systems? In other words, how many pixels are sufficient to build an accurate vision-based vehicle classification system? Aim: To study the effect of spatial resolution on the accuracy and performance of image classification methods. Proposed System: present a comprehensive study of the effect of these two spatial characteristics ( Dimension and color ) of digital images on the vision-based vehicle classification process in terms of accuracy and performance. For Bag-of-Visual Words (BoVW) [13], Vector of Locally Aggregated Descriptors (VLAD) [14] and Fisher Vector (FV) [15] it is recommended to use the DSIFT descriptor. The ResNet/Convolutional Neural Network (CNN), architecture is considered the top classification method with an accuracy rate of 95.12% compare to BoVW, VLAD an...
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