Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks. In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime ...
© 2020 IEEE. This paper addresses the problem of detecting humans in a point cloud taken with a 3D-L...
In this paper, we introduce a system framework which can automatically interpret large point cloud ...
A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a...
The goal of this Master's Thesis is to successfully detect and classify humans in a LiDAR data strea...
The detection of objects, or persons, is a common task in the fields of environment surveillance, ob...
In this paper we present a new approach for object classification in continuously streamed Lidar poi...
Pedestrian detection and tracking plays an essential role in autonomous vehicles and mobile service ...
This paper presents a system for online learning of human classifiers by mobile service robots using...
Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This ...
This paper deals with human detection in the LiDAR data using the YOLO object detection neural netwo...
The current state of the art of traffic tracking is based on the use of video, and requires extensiv...
In this paper, we propose an approach on real-time 3D people surveillance, with probabilistic foregr...
Pedestrian detection in driving assistant system refers to obtain the 3-d coordinate of the pedestri...
Detection of vehicles on 3D point clouds is performed by using the algorithm presented in this work....
This paper presents a vision-based people detection system for improving safety in heavy machines. W...
© 2020 IEEE. This paper addresses the problem of detecting humans in a point cloud taken with a 3D-L...
In this paper, we introduce a system framework which can automatically interpret large point cloud ...
A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a...
The goal of this Master's Thesis is to successfully detect and classify humans in a LiDAR data strea...
The detection of objects, or persons, is a common task in the fields of environment surveillance, ob...
In this paper we present a new approach for object classification in continuously streamed Lidar poi...
Pedestrian detection and tracking plays an essential role in autonomous vehicles and mobile service ...
This paper presents a system for online learning of human classifiers by mobile service robots using...
Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This ...
This paper deals with human detection in the LiDAR data using the YOLO object detection neural netwo...
The current state of the art of traffic tracking is based on the use of video, and requires extensiv...
In this paper, we propose an approach on real-time 3D people surveillance, with probabilistic foregr...
Pedestrian detection in driving assistant system refers to obtain the 3-d coordinate of the pedestri...
Detection of vehicles on 3D point clouds is performed by using the algorithm presented in this work....
This paper presents a vision-based people detection system for improving safety in heavy machines. W...
© 2020 IEEE. This paper addresses the problem of detecting humans in a point cloud taken with a 3D-L...
In this paper, we introduce a system framework which can automatically interpret large point cloud ...
A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a...