7 papers
Driver Anomaly Detection: A Dataset and Contrastive Learning Approach
Okan Köpüklü, Jiapeng Zheng, Hang Xu +1
Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers' actions (i.e., any action deviating from…
Deep Attention Based Semi-Supervised 2D-Pose Estimation for Surgical Instruments
Mert Kayhan, Okan Köpüklü, Mhd Hasan Sarhan +3
For many practical problems and applications, it is not feasible to create a vast and accurately labeled dataset, which restricts the application of deep learning in many areas. Se…
Unsupervised Monocular Depth Prediction for Indoor Continuous Video Streams
Yinglong Feng, Shuncheng Wu, Okan Köpüklü +2
This paper studies unsupervised monocular depth prediction problem. Most of existing unsupervised depth prediction algorithms are developed for outdoor scenarios, while the depth p…
Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos
Okan Köpüklü, Fabian Herzog, Gerhard Rigoll
Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling. In this sur…
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach
Neslihan Kose, Okan Kopuklu, Alexander Unnervik +1
Many road accidents occur due to distracted drivers. Today, driver monitoring is essential even for the latest autonomous vehicles to alert distracted drivers in order to take over…
Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs
Okan Köpüklü, Yao Rong, Gerhard Rigoll
The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication b…