no code implementations • 14 Sep 2023 • Syed Sha Qutub, Neslihan Kose, Rafael Rosales, Michael Paulitsch, Korbinian Hagn, Florian Geissler, Yang Peng, Gereon Hinz, Alois Knoll
The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models.
no code implementations • 9 Dec 2022 • Neslihan Kose, Ranganath Krishnan, Akash Dhamasia, Omesh Tickoo, Michael Paulitsch
Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.
no code implementations • 27 Aug 2020 • Juan Diego Ortega, Neslihan Kose, Paola Cañas, Min-An Chao, Alexander Unnervik, Marcos Nieto, Oihana Otaegui, Luis Salgado
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods.
no code implementations • 2 Mar 2020 • Okan Köpüklü, Thomas Ledwon, Yao Rong, Neslihan Kose, Gerhard Rigoll
In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues.
no code implementations • 18 Jul 2019 • Neslihan Kose, Okan Kopuklu, Alexander Unnervik, Gerhard Rigoll
Experiments show that our approach outperforms the state-of-the art results on the Distracted Driver Dataset (96. 31%), with an accuracy of 99. 10% for 10-class classification while providing real-time performance.
2 code implementations • 4 Apr 2019 • Okan Köpüklü, Neslihan Kose, Ahmet Gunduz, Gerhard Rigoll
Recently, convolutional neural networks with 3D kernels (3D CNNs) have been very popular in computer vision community as a result of their superior ability of extracting spatio-temporal features within video frames compared to 2D CNNs.
Ranked #2 on Action Recognition In Videos on UCF101
5 code implementations • 29 Jan 2019 • Okan Köpüklü, Ahmet Gunduz, Neslihan Kose, Gerhard Rigoll
We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures.
Ranked #1 on Hand Gesture Recognition on EgoGesture