Proposal of a Computer Vision System to Detect and Track Vehicles in Real Time Using an Embedded Platform Enabled with a Graphical Processing Unit

This paper presents a development proposal of a systemcapable of detecting, localizing and tracking vehicles inreal time using computer vision algorithms in an embeddedplatform for an advanced driver assistance system (ADAS). This project will use a Flea3 monocular color camera fromPointGrey. The video will be processed using support vectormachines (SVM) and convolutional neural networks (CNN). These algorithms will be implemented in a Jetson TK1 developmentboard from Nvidia which has a 192-core KeplerGPU, a quad-core ARM Cortex A15, 2 GB of RAM, 16 GBof internal memory, and a set of basic peripherals for automotiveapplications. Finally, this project will use librariessuch as: CUDA, OpenCV, and CudNN which are optimizedfor the Jetson TK1 as well as the LibSVM and Caffe librariesto train SVM and CNN models.