© 2018 Australasian Robotics and Automation Association. All rights reserved. In this paper we introduce a generic method for people and vehicle detection using LiDAR data only, leveraging a pre-trained Convolutional Neural Network (CNN) from the RGB domain. Typically with machine learning algorithms, there is an inherent trade-off between the amount of training data available and the need for engineered features. The current state-of-the-art object detection and classification heavily rely on deep CNNs trained on enormous RGB image datasets. To take advantage of this inbuilt knowledge, we propose to fine-tune You only look once (YOLO) network transferring its understanding about object shapes to upsampled LiDAR images. Our method creates a...
The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate predic...
This article aims at demonstrating the feasibility of modern deep learning techniques for the real-t...
In this paper, we are presenting a proof of concept of our system for training of the YOLOv3 neural ...
This paper deals with human detection in the LiDAR data using the YOLO object detection neural netwo...
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars a...
The performance of autonomous agents in both commercial and consumer applications increases along wi...
In this paper we present a new approach for object classification in continuously streamed Lidar poi...
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and l...
International audienceExisting neural network-based object detection approaches process LiDAR point ...
International audienceAccurate 3D object detection is a key part of the perception module for autono...
It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects...
In recent years, there has been a significant improvement in the detection, identification and class...
3D object detection is a fundamental component in the autonomous driving perception pipeline. While ...
Abstract — Why is pedestrian detection still very challenging in realistic scenes? How much would a ...
The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate predic...
This article aims at demonstrating the feasibility of modern deep learning techniques for the real-t...
In this paper, we are presenting a proof of concept of our system for training of the YOLOv3 neural ...
This paper deals with human detection in the LiDAR data using the YOLO object detection neural netwo...
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars a...
The performance of autonomous agents in both commercial and consumer applications increases along wi...
In this paper we present a new approach for object classification in continuously streamed Lidar poi...
Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and l...
International audienceExisting neural network-based object detection approaches process LiDAR point ...
International audienceAccurate 3D object detection is a key part of the perception module for autono...
It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects...
In recent years, there has been a significant improvement in the detection, identification and class...
3D object detection is a fundamental component in the autonomous driving perception pipeline. While ...
Abstract — Why is pedestrian detection still very challenging in realistic scenes? How much would a ...
The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate predic...
This article aims at demonstrating the feasibility of modern deep learning techniques for the real-t...
In this paper, we are presenting a proof of concept of our system for training of the YOLOv3 neural ...