The deep learning technique has proven to be effective in the classification and localization
of objects on the image or ground plane over time. The strength of the technique's features …

The ability to recognize, localize and track dynamic objects in a scene is fundamental to
many real-world applications, such as self-driving and robotic systems. Yet, traditional …

Semantic classes can be either things (objects with a well-defined shape, eg car, person) or
stuff (amorphous background regions, eg grass, sky). While lots of classification and …

Semantic image segmentation is an essential component of modern autonomous driving
systems, as an accurate understanding of the surrounding scene is crucial to navigation and …

Abstract Current multi-category Multiple Object Tracking (MOT) metrics use class labels to
group tracking results for per-class evaluation. Similarly, MOT methods typically only …

Online multi-object tracking (MOT) is extremely important for high-level spatial reasoning
and path planning for autonomous and highly-automated vehicles. In this paper, we present …

The ability to extract individual objects in the scene is key for a large number of autonomous
navigation systems such as mobile robots or autonomous cars. Such systems navigating in …

Tracking in urban street scenes plays a central role in autonomous systems such as self-
driving cars. Most of the current vision-based tracking methods perform tracking in the image …

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 …

Methods tackling multi-object tracking need to estimate the number of targets in the sensing
area as well as to estimate their continuous state. While the majority of existing methods …