Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Rawashdeh, Samir A."'
Autor:
Shair, Zaid A. El, Rawashdeh, Samir A.
In this data article, we introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset. This dataset provides a synchronized stream of event data and grayscale images of traffic scenes, captured using the Dynamic and Active-Pix
Externí odkaz:
http://arxiv.org/abs/2407.20446
Autor:
El-Shair, Zaid A., Abu-raddaha, Abdalmalek, Cofield, Aaron, Alawneh, Hisham, Aladem, Mohamed, Hamzeh, Yazan, Rawashdeh, Samir A.
Robust perception is critical for autonomous driving, especially under adverse weather and lighting conditions that commonly occur in real-world environments. In this paper, we introduce the Stereo Image Dataset (SID), a large-scale stereo-image data
Externí odkaz:
http://arxiv.org/abs/2407.04908
Publikováno v:
IEEE Access, 2023
Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. T
Externí odkaz:
http://arxiv.org/abs/2303.10471
Autor:
El-Shair, Zaid, Rawashdeh, Samir
Publikováno v:
Optical Engineering 62(3), 031209 (22 December 2022)
Event-based vision has been rapidly growing in recent years justified by the unique characteristics it presents such as its high temporal resolutions (~1us), high dynamic range (>120dB), and output latency of only a few microseconds. This work furthe
Externí odkaz:
http://arxiv.org/abs/2212.14289
Over the last few decades, many architectures have been developed that harness the power of neural networks to detect objects in near real-time. Training such systems requires substantial time across multiple GPUs and massive labeled training dataset
Externí odkaz:
http://arxiv.org/abs/2102.04582
Autor:
Chennupati, Sumanth, Narayanan, Venkatraman, Sistu, Ganesh, Yogamani, Senthil, Rawashdeh, Samir A
Panoptic Segmentation aims to provide an understanding of background (stuff) and instances of objects (things) at a pixel level. It combines the separate tasks of semantic segmentation (pixel level classification) and instance segmentation to build a
Externí odkaz:
http://arxiv.org/abs/2010.11681
Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computational complexity. Current work on multi-task learning networks focus on pr
Externí odkaz:
http://arxiv.org/abs/1904.08492
Autor:
Sistu, Ganesh, Leang, Isabelle, Chennupati, Sumanth, Yogamani, Senthil, Hughes, Ciaran, Milz, Stefan, Rawashdeh, Samir
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and
Externí odkaz:
http://arxiv.org/abs/1902.03589
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task whi
Externí odkaz:
http://arxiv.org/abs/1901.05808
Publikováno v:
Journal of Imaging; Sep2024, Vol. 10 Issue 9, p227, 22p
