Image understanding enables better reconstruction of the physical world from images and
videos. Existing methods focus largely on geometry and visual appearance of the …

In this paper we present Semantic Stixels, a novel vision-based scene model geared
towards automated driving. Our model jointly infers the geometric and semantic layout of a …

Drones equipped with cameras have been fast deployed to a wide range of applications,
such as agriculture, aerial photography, fast delivery, and surveillance. As the core steps in …

Object tracking is one of the key components of the perception system of autonomous cars
and ADASs. With tracking, an ego-vehicle can make a prediction about the location of …

In recent years, an increasing number of school children is beginning to learn about robotics
in the classroom in order to stir their interest in STEM professions. Teachers rely on simple …

Abstract Vehicle Load Monitoring (VLM) on entire long-span bridge decks presents
significant challenges due to the spatial and temporal randomness of vehicles. Existing VLM …

This paper proposes an online multi-object tracking algorithm for image observations using
a top-down Bayesian formulation that seamlessly integrates state estimation, track …

In autonomous driving community, numerous benchmarks have been established to assist
the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation …

Connected and autonomous vehicles (CAVs) have recently attracted a significant amount of
attention both from researchers and industry. Numerous studies targeting algorithms …

Multi-object tracking (MOT) is a significant and widespread research field in image
processing and computer vision. The goal of the MOT task consists in predicting the …