The advancement of technology in the field of autonomous navigation for robots is one of the
leading trends nowadays, where we need to encounter huge amount of unpredictability that …

Why is pedestrian detection still very challenging in realistic scenes? How much would a
successful solution to monocular depth inference aid pedestrian detection? In order to …

New processing methods based on artificial intelligence (AI) and deep learning are
replacing traditional computer vision algorithms. The more advanced systems can process …

Self-supervised monocular depth estimation methods typically rely on the reprojection error
to capture geometric relationships between successive frames in static environments …

Segmenting humans in 3D indoor scenes has become increasingly important with the rise of
human-centered robotics and AR/VR applications. To this end, we propose the task of joint …

Object detection is an essential task for autonomous robots operating in dynamic and
changing environments. A robot should be able to detect objects in the presence of sensor …

An energy-efficient convolutional neural network (CNN) processor is proposed for real-time
image segmentation on mobile devices. The proposed processor utilizes Region of Interest …

Stereo vision is widely deployed on robots and drones to enable depth estimation at a low
cost. The combination of lightweight deep neural network (DNN) and cost volumes algorithm …

This paper proposes a real-time indoor surveillance system which installs multiple depth
cameras from vertical top-view to track humans. This system leads to a novel framework to …

This article proposes a multibody electromagnetic (EM) model for the quantitative evaluation
of the influence of multiple human bodies in the surroundings of a radio link. Modeling of …