Smart computer vision systems for autonomous driving
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 res...
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| Format: | Thesis |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/78819 |
| _version_ | 1811678862448263168 |
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| author | Rajasekaran Neetha |
| author2 | Justin Dauwels |
| author_facet | Justin Dauwels Rajasekaran Neetha |
| author_sort | Rajasekaran Neetha |
| collection | NTU |
| description | 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 |
| first_indexed | 2024-10-01T03:00:00Z |
| format | Thesis |
| id | ntu-10356/78819 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:00:00Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/788192023-07-04T16:21:07Z Smart computer vision systems for autonomous driving Rajasekaran Neetha Justin Dauwels School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics 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 Master of Science (Computer Control and Automation) 2019-07-01T00:13:30Z 2019-07-01T00:13:30Z 2019 Thesis http://hdl.handle.net/10356/78819 en 86 p. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Rajasekaran Neetha Smart computer vision systems for autonomous driving |
| title | Smart computer vision systems for autonomous driving |
| title_full | Smart computer vision systems for autonomous driving |
| title_fullStr | Smart computer vision systems for autonomous driving |
| title_full_unstemmed | Smart computer vision systems for autonomous driving |
| title_short | Smart computer vision systems for autonomous driving |
| title_sort | smart computer vision systems for autonomous driving |
| topic | Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics |
| url | http://hdl.handle.net/10356/78819 |
| work_keys_str_mv | AT rajasekaranneetha smartcomputervisionsystemsforautonomousdriving |