Zobrazeno 1 - 10
of 381
pro vyhledávání: '"traffic scene"'
Publikováno v:
Tongxin xuebao, Vol 44, Pp 213-225 (2023)
Aiming at the problems of discontinuous segmentation of thin strip objects that were easy to blend into the surrounding background and a large number of model parameters in the semantic segmentation algorithm of traffic scenes, a lightweight Transfor
Externí odkaz:
https://doaj.org/article/bfef6ba2bac945bd84808fd515a0ee56
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 244-260 (2023)
Automated identification of the relationships between traffic actors and surrounding objects, in order to describe their behavior and predict their intentions, has become the focus of increasing attention in the field of autonomous driving. Therefore
Externí odkaz:
https://doaj.org/article/a172a00362964a3ba4f04e840f7f0051
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2022, Iss 1, Pp 1-21 (2022)
Abstract In practical road traffic scene, targets usually face high ground clutter, high and variable motion, high nonlinearity, which lead to targets tracking or identification challenging. What’s more, tracking and identification are usually inte
Externí odkaz:
https://doaj.org/article/4773809c063448c49a84105816ae770b
Akademický článek
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Akademický článek
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Publikováno v:
Archives of Transport, Vol 58, Iss 2, Pp 81-97 (2021)
Level crossing is an element of the transport infrastructure of a particular type. This is where streams of regulated and unregulated traffic interact. Vehicles of regulated, rail traffic affect on unregulated, road traffic vehicles. This process tak
Externí odkaz:
https://doaj.org/article/46e23901545b496b98495437ae2fb660
Publikováno v:
IEEE Access, Vol 9, Pp 1420-1427 (2021)
The traffic scene understanding is the core technology in Intelligent Transportation Systems (ITS) and Advanced Driver Assistance System (ADAS), and it is becoming increasingly important for smart or autonomous vehicles. The recent methods for traffi
Externí odkaz:
https://doaj.org/article/201db0fcd4f9451d845991a90a5a3c16
Autor:
Sandra Aigner, Marco Körner
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 2, Iss 2, Pp 78-98 (2020)
This paper analyzes in detail how different loss functions influence the generalization abilities of a deep learning-based next frame prediction model for traffic scenes. Our prediction model is a convolutional long-short term memory (ConvLSTM) netwo
Externí odkaz:
https://doaj.org/article/58b3690794c44895a6e8cc51afa25d08
Publikováno v:
IEEE Access, Vol 8, Pp 80527-80535 (2020)
Occlusion caused by multi-object interaction makes the traffic scene understanding intractable. In this paper, we focus on predicting the visibility status of vehicle in the framework of causality perception. The visibility fluent is employed to pres
Externí odkaz:
https://doaj.org/article/889e3cef01414ba9a0ae385f1d80bc7f
Publikováno v:
Data, Vol 8, Iss 1, p 16 (2023)
Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In t
Externí odkaz:
https://doaj.org/article/a087e5ab977d4f1fb8c910ea747fb8f3