Road Traffic Analysis on the CNN Universal Machine

Abstract

CNN Universal Machines that contain two different processors working interactively with each other, have an important impact for image processing applications with their advanced computing features. These processors are named as the ACE16k processor which is the hardware implementation of cellular neural networks and the Digital Signal Processor (DSP). Bio-inspired (Bi-i) Cellular Vision System is also a CNN Universal Machine and its standalone architecture is built on CNN-type (ACE16k) and DSP-type microprocessors. In this study, certain objects in moving images are detected and their features are extracted. By using these features, an algorithm that finds out the path of moving vehicles and determines the traffic density is implemented on the Bi-i Cellular Vision System. Finally, the output images and the data related with traffic density which are obtained as results of this implementation are evaluated.