Zobrazeno 1 - 10
of 2 395
pro vyhledávání: '"Kressel A"'
Compartmentalization is a form of defensive software design in which an application is broken down into isolated but communicating components. Retrofitting compartmentalization into existing applications is often thought to be expensive from the engi
Externí odkaz:
http://arxiv.org/abs/2309.11332
Despite the significant research efforts on trajectory prediction for automated driving, limited work exists on assessing the prediction reliability. To address this limitation we propose an approach that covers two sources of error, namely novel sit
Externí odkaz:
http://arxiv.org/abs/2308.01707
Accurate 3D human pose estimation (3D HPE) is crucial for enabling autonomous vehicles (AVs) to make informed decisions and respond proactively in critical road scenarios. Promising results of 3D HPE have been gained in several domains such as human-
Externí odkaz:
http://arxiv.org/abs/2307.14889
Autor:
Saira Butt, Amy B. Kressel, Brian L. Haines, Katherine Merrill, Amber M. Ryan, Kenneth C. Gavina, Bree Weaver, Michael Kays, Molly Tieman, Margaret Muciarelli, Phillip Clapham
Publikováno v:
Infection Prevention in Practice, Vol 6, Iss 4, Pp 100417- (2024)
Summary: Background: The United States Food and Drug Administration recently announced a national blood culture (BC) bottle shortage; the exact date of restoration is still being determined. Aim: Implement a workflow to mitigate the BC bottle shortag
Externí odkaz:
https://doaj.org/article/87fa6906182e47e7b5bde0ed72384e84
This paper presents a novel approach to pedestrian trajectory prediction for on-board camera systems, which utilizes behavioral features of pedestrians that can be inferred from visual observations. Our proposed method, called Behavior-Aware Pedestri
Externí odkaz:
http://arxiv.org/abs/2210.11999
Human intuition allows to detect abnormal driving scenarios in situations they never experienced before. Like humans detect those abnormal situations and take countermeasures to prevent collisions, self-driving cars need anomaly detection mechanisms.
Externí odkaz:
http://arxiv.org/abs/2209.01838
In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a
Externí odkaz:
http://arxiv.org/abs/2207.00499
Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driv
Externí odkaz:
http://arxiv.org/abs/2110.07922
We present a simple, yet effective, approach for self-supervised 3D human pose estimation. Unlike the prior work, we explore the temporal information next to the multi-view self-supervision. During training, we rely on triangulating 2D body pose esti
Externí odkaz:
http://arxiv.org/abs/2110.07578
We present a self-supervised learning algorithm for 3D human pose estimation of a single person based on a multiple-view camera system and 2D body pose estimates for each view. To train our model, represented by a deep neural network, we propose a fo
Externí odkaz:
http://arxiv.org/abs/2108.07777