Zobrazeno 1 - 10
of 177
pro vyhledávání: '"Ostadabbas, Sarah"'
Autor:
Hatamimajoumerd, Elaheh, Kakhaki, Pooria Daneshvar, Huang, Xiaofei, Luan, Lingfei, Amraee, Somaieh, Ostadabbas, Sarah
Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis. Precise action recognition in infants serves a m
Externí odkaz:
http://arxiv.org/abs/2311.12300
Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development. In the case of preterm infants, NNS behavior is a key compo
Externí odkaz:
http://arxiv.org/abs/2310.16138
Respiration is a critical vital sign for infants, and continuous respiratory monitoring is particularly important for newborns. However, neonates are sensitive and contact-based sensors present challenges in comfort, hygiene, and skin health, especia
Externí odkaz:
http://arxiv.org/abs/2307.13110
Autor:
Bai, Xiangyu, Luo, Yedi, Jiang, Le, Gupta, Aniket, Kaveti, Pushyami, Singh, Hanumant, Ostadabbas, Sarah
Modern autonomous systems require extensive testing to ensure reliability and build trust in ground vehicles. However, testing these systems in the real-world is challenging due to the lack of large and diverse datasets, especially in edge cases. The
Externí odkaz:
http://arxiv.org/abs/2306.02631
Autor:
Luo, Yedi, Bai, Xiangyu, Jiang, Le, Gupta, Aniket, Mortin, Eric, Singh, Hanumant, Ostadabbas, Sarah
This paper presents a novel approach, TeFS (Temporal-controlled Frame Swap), to generate synthetic stereo driving data for visual simultaneous localization and mapping (vSLAM) tasks. TeFS is designed to overcome the lack of native stereo vision suppo
Externí odkaz:
http://arxiv.org/abs/2306.01704
Autor:
Jiang, Le, Ostadabbas, Sarah
Animal pose estimation has become a crucial area of research, but the scarcity of annotated data is a significant challenge in developing accurate models. Synthetic data has emerged as a promising alternative, but it frequently exhibits domain discre
Externí odkaz:
http://arxiv.org/abs/2305.17845
Autor:
Zhu, Shaotong, Wan, Michael, Hatamimajoumerd, Elaheh, Jain, Kashish, Zlota, Samuel, Kamath, Cholpady Vikram, Rowan, Cassandra B., Grace, Emma C., Goodwin, Matthew S., Hayes, Marie J., Schwartz-Mette, Rebecca A., Zimmerman, Emily, Ostadabbas, Sarah
We present an end-to-end computer vision pipeline to detect non-nutritive sucking (NNS) -- an infant sucking pattern with no nutrition delivered -- as a potential biomarker for developmental delays, using off-the-shelf baby monitor video footage. One
Externí odkaz:
http://arxiv.org/abs/2303.16867
We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical. Specifically, we use a comb
Externí odkaz:
http://arxiv.org/abs/2210.15022
Accurately annotated image datasets are essential components for studying animal behaviors from their poses. Compared to the number of species we know and may exist, the existing labeled pose datasets cover only a small portion of them, while buildin
Externí odkaz:
http://arxiv.org/abs/2208.13944
Autor:
Wang, Zhouping, Ostadabbas, Sarah
3D Human body pose and shape estimation within a temporal sequence can be quite critical for understanding human behavior. Despite the significant progress in human pose estimation in the recent years, which are often based on single images or videos
Externí odkaz:
http://arxiv.org/abs/2207.12537