Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78819
Title: Smart computer vision systems for autonomous driving
Authors: Rajasekaran Neetha
Keywords: Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Issue Date: 2019
Abstract: The effect of Artificial Intelligence is expanding widely over several areas includingautonomous vehicles, transportation, automation of manufacturing industries. The autonomous cars and unmanned vehicles have been heavily influenced by Artificial Intelligence and they can be viewed as the results of Artificial Intelligence in the field oftransportation. These advances in the autonomous vehicles are extremely beneficial and hence detecting the Intention of the Pedestrian who is crossing the Autonomous vehicle is very crucial, for the safety of the pedestrians. This dissertation has been obtained its focus from therequirements of the Autonomous vehicles and latest machine learning methods that could be used to precisely predict the Intention of the Pedestrian. The scope of this dissertation covers to design a Neural Network system using a vision basedsystem calledReal time Pose Estimation method and predict the next step of the Pedestrian. The design of the various models used for comparison to prove the efficient computation times have been implemented
URI: http://hdl.handle.net/10356/78819
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Dissertation-smart vision system-corrected_v1 2.pdf
  Restricted Access
Main article2.4 MBAdobe PDFView/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.