Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Morris, Brendan"'
Autor:
Rahman, Zillur, Morris, Brendan Tran
Publikováno v:
2023 IEEE International Conference on Intelligent Transportation Systems (ITSC)
Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Despite advances from image processing to deep learning based models, algorithm performance is highly dependent on training data ma
Externí odkaz:
http://arxiv.org/abs/2307.06853
Publikováno v:
2022 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
In the last decade, research in the field of autonomous vehicles has grown immensely, and there is a wealth of information available for researchers to rapidly establish an autonomous vehicle platform for basic maneuvers. In this paper, we design, im
Externí odkaz:
http://arxiv.org/abs/2210.12115
Autor:
Morris, Brendan Tran.
Publikováno v:
Connect to a 24 p. preview or full text in PDF format. Access restricted to UC IP addresses
Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed May 6, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 121-129).
Title from first page of PDF file (viewed May 6, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 121-129).
Externí odkaz:
http://wwwlib.umi.com/cr/ucsd/fullcit?p3398750
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications
Externí odkaz:
http://arxiv.org/abs/2107.00422
In applications such as object tracking, time-series data inevitably carry missing observations. Following the success of deep learning-based models for various sequence learning tasks, these models increasingly replace classic approaches in object t
Externí odkaz:
http://arxiv.org/abs/2106.16009
In tasks such as tracking, time-series data inevitably carry missing observations. While traditional tracking approaches can handle missing observations, recurrent neural networks (RNNs) are designed to receive input data in every step. Furthermore,
Externí odkaz:
http://arxiv.org/abs/2103.11747
Autor:
Parmar, Paritosh, Morris, Brendan
Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understa
Externí odkaz:
http://arxiv.org/abs/2102.07355
Can a computer determine a piano player's skill level? Is it preferable to base this assessment on visual analysis of the player's performance or should we trust our ears over our eyes? Since current CNNs have difficulty processing long video videos,
Externí odkaz:
http://arxiv.org/abs/2101.04884
Autor:
Parmar, Paritosh, Morris, Brendan
Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared
Externí odkaz:
http://arxiv.org/abs/1912.04430
Autor:
Parmar, Paritosh, Morris, Brendan Tran
Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the fin
Externí odkaz:
http://arxiv.org/abs/1904.04346