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|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|
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|Dissertation-smart vision system-corrected_v1 2.pdf|
|Main article||2.4 MB||Adobe PDF||View/Open|
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