Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Source : 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Institute of Electrical and Electronics Engineers Inc. (2017)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042748718&doi=10.1109%2fICACCI.2017.8126154&partnerID=40&md5=b5af1d65f21630e6049fba79ba61d073
ISBN : 9781509063673
Keywords : Computer vision techniques, Convolutional Neural Networks (CNN), Cost effectiveness, Deep learning, Encoder-decoder architecture, Neural networks, Obstacle classification, Obstacle detectors, Research and development, Research laboratories, Research opportunities, Semantic segmentation, Semantics, Vision based navigation
Campus : Coimbatore
School : Computational Engineering and Networking
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Year : 2017
Abstract : With the rising trend in research and development of autonomous vehicles, it is important to keep in mind the cost effectiveness of the system. The cost of high-end sensor technologies being astronomically expensive, the research opportunities are restricted to a select few of high-tech companies and research laboratories such as Google, Tesla, Ford, and the likes of it. Hence our main focus is to develop an autonomous system suitable for academic and research purposes as well. This can be achieved by using available sensors such as the monocular cameras. The existing computer vision techniques along with the deep learning tools like Convolutional Neural Network (CNN) can together be used for developing a robust vision based autonomous driving system. The proposed method uses the SegNet encoder-decoder architecture for pixel-wise semantic segmentation of the video frame followed by an obstacle detection algorithm. The entire algorithm was implemented and tested on a mobile embedded platform of NVIDIA's Jetson TK1.
Cite this Research Publication : N. Deepika and Sajith Variyar V. V., “Obstacle Classification and Detection for Vision Based Navigation for Autonomous Driving”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.