Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. Almost at the same …

Object detection has been one of the most active topics in computer vision for the past years.
Recent works have mainly focused on pushing the state-of-the-art in the general-purpose …

This paper presents a solution to the current challenges of the imaging radar to respond the
demands of autonomy for detection and classification of targets in radar imagery, which …

In this paper, we present a Remote Sens. approach to localize parking cars in a city in order
to enable the development of parking space availability models. We propose to use high …

Robust evidence suggests that humans explore their environment using a combination of
topological landmarks and coarse-grained path integration. This approach relies on …

The relatively complex task of detecting 3D objects is essential in the realm of autonomous
driving. The related algorithmic processes generally produce an output that consists of a …

Recently, infrared object detection (IOD) has been extensively studied due to the rapid
growth of deep neural networks (DNNs). An adversarial attack using imperceptible …

State-of-the-art hardware masking approaches like threshold implementations and domain-
oriented masking provide a guaranteed level of security even in the presence of glitches …

Deep neural networks achieve state-of-the-art results in many different problem settings by
exploiting vast amounts of training data. However, collecting, storing and—in the case of …