Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
Multiple object tracking: A literature review
Abstract Multiple Object Tracking (MOT) has gained increasing attention due to its academic
and commercial potential. Although different approaches have been proposed to tackle this …
and commercial potential. Although different approaches have been proposed to tackle this …
Siamese masked autoencoders
Establishing correspondence between images or scenes is a significant challenge in
computer vision, especially given occlusions, viewpoint changes, and varying object …
computer vision, especially given occlusions, viewpoint changes, and varying object …
Motiontrack: Learning robust short-term and long-term motions for multi-object tracking
The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous
trajectory for each target. Existing methods often learn reliable motion patterns to match the …
trajectory for each target. Existing methods often learn reliable motion patterns to match the …
Argoverse: 3d tracking and forecasting with rich maps
We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …
Deep learning in video multi-object tracking: A survey
Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …
different objects in a sequence, usually a video. In recent years, with the rise of Deep …
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs
A Rosinol, A Violette, M Abate… - … Journal of Robotics …, 2021 - journals.sagepub.com
Humans are able to form a complex mental model of the environment they move in. This
mental model captures geometric and semantic aspects of the scene, describes the …
mental model captures geometric and semantic aspects of the scene, describes the …
Learning correspondence from the cycle-consistency of time
We introduce a self-supervised method for learning visual correspondence from unlabeled
video. The main idea is to use cycle-consistency in time as free supervisory signal for …
video. The main idea is to use cycle-consistency in time as free supervisory signal for …
Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
The rampant coronavirus disease 2019 (COVID-19) has brought global crisis with its deadly
spread to more than 180 countries, and about 3,519,901 confirmed cases along with …
spread to more than 180 countries, and about 3,519,901 confirmed cases along with …
Vision meets drones: A challenge
In this paper we present a large-scale visual object detection and tracking benchmark,
named VisDrone2018, aiming at advancing visual understanding tasks on the drone …
named VisDrone2018, aiming at advancing visual understanding tasks on the drone …