49 citations · 160 across the 14 of their papers we have counts for
14 papers
ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer
Eslam Mohamed, Ahmad El-Sallab
We propose ST-DETR, a Spatio-Temporal Transformer-based architecture for object detection from a sequence of temporal frames. We treat the temporal frames as sequences in both spac…
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving
Hazem Rashed, Mariam Essam, Maha Mohamed +2
Detection of moving objects is a very important task in autonomous driving systems. After the perception phase, motion planning is typically performed in Bird's Eye View (BEV) spac…
MODETR: Moving Object Detection with Transformers
Eslam Mohamed, Ahmad El-Sallab
Moving Object Detection (MOD) is a crucial task for the Autonomous Driving pipeline. MOD is usually handled via 2-stream convolutional architectures that incorporates both appearan…
Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation
Eslam Mohamed, Ahmed El-Sallab
Moving objects have special importance for Autonomous Driving tasks. Detecting moving objects can be posed as Moving Object Segmentation, by segmenting the object pixels, or Moving…
VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving
Hazem Rashed, Ahmad El Sallab, Senthil Yogamani
Moving object Detection (MOD) is a critical task in autonomous driving as moving agents around the ego-vehicle need to be accurately detected for safe trajectory planning. It also…
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving
Mohamed Ramzy, Hazem Rashed, Ahmad El Sallab +1
Moving Object Detection (MOD) is a critical task for autonomous vehicles as moving objects represent higher collision risk than static ones. The trajectory of the ego-vehicle is pl…
Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation
Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran +1
Data scarcity is a bottleneck to machine learning-based perception modules, usually tackled by augmenting real data with synthetic data from simulators. Realistic models of the veh…
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
Hazem Rashed, Mohamed Ramzy, Victor Vaquero +3
Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned…
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
Khaled El Madawy, Hazem Rashed, Ahmad El Sallab +3
LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or…
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
Mohammed Abdou, Mahmoud Elkhateeb, Ibrahim Sobh +1
3D semantic scene labeling is a fundamental task for Autonomous Driving. Recent work shows the capability of Deep Neural Networks in labeling 3D point sets provided by sensors like…
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran +1
In the autonomous driving domain, data collection and annotation from real vehicles are expensive and sometimes unsafe. Simulators are often used for data augmentation, which requi…
Optical Flow augmented Semantic Segmentation networks for Automated Driving
Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab +2
Motion is a dominant cue in automated driving systems. Optical flow is typically computed to detect moving objects and to estimate depth using triangulation. In this paper, our mot…
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
Waleed Ali, Sherif Abdelkarim, Mohamed Zahran +2
Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding enviro…
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
Mennatullah Siam, Heba Mahgoub, Mohamed Zahran +3
We propose a novel multi-task learning system that combines appearance and motion cues for a better semantic reasoning of the environment. A unified architecture for joint vehicle…