The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）
Fulong Ma, Peng Hou, Yuxuan Liu, Yang Liu, Ming Liu, Jun Ma
Road curbs are considered as one of the crucial and ubiquitous traffic features, which are essential for ensuring the safety of autonomous vehicles. Current methods for detecting curbs primarily rely on camera imagery or LiDAR point clouds. Image-based methods are vulnerable to fluctuations in lighting conditions and exhibit poor robustness, while methods based on point clouds circumvent the issues associated with lighting variations. However, it is the typical case that significant processing delays are encountered due to the voluminous amount of 3D points contained in each frame of the point cloud data. Furthermore, the inherently unstructured characteristics of point clouds poses challenges for integrating the latest deep learning advancements into point cloud data applications. To address these issues, this work proposes an annotation-free curb detection method leveraging Altitude Difference Image (ADI), which effectively mitigates the aforementioned challenges. Given that methods based on deep learning generally demand extensive, manually annotated datasets, which are both expensive and labor-intensive to create, we present an Automatic Curb Annotator (ACA) module. This module utilizes a deterministic curb detection algorithm to automatically generate a vast quantity of training data. Consequently, it facilitates the training of the curb detection model without necessitating any manual annotation of data. Finally, by incorporating a post-processing module, we manage to achieve state-of-the-art results on the KITTI 3D curb dataset with considerably reduced processing delays compared to existing methods, which underscores the effectiveness of our approach in curb detection tasks.
Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Rec…
Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net
Chair of Cartography and Visual Analytics, Technical University of Munich（慕尼黑工业大学制图与视觉分析主席）；Institute of Cartography and Geoinformatics, Leibniz University Hannover（汉诺威莱布尼茨大学制图与地理信息研究所）；Computer Vision Group, Technical University of Munich（慕尼黑工业大学计算机视觉组）
University of Southern California（南加州大学）；Institute for Creative Technologies（创意技术研究所）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；New York University（纽约大学）
At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locations and ground materials (Chen et al., 2019). The ultimate goal is to create realistic virtual environments and provide the necessary information for simulation. We tested the generalizability of the previously proposed framework using a database created under the U.S. Army's One World Terrain (OWT) project with a variety of landscapes (i.e., various buildings styles, types of vegetation, and urban density) and diffe…
Unmanned aerial vehicles (UAVs) with mounted cameras have the advantage of capturing aerial (bird-view) images. The availability of aerial visual data and the recent advances in object detection algorithms led the computer vision community to focus on object detection tasks on aerial images. As a result of this, several aerial datasets have been introduced, including visual data with object annotations. UAVs are used solely as flying-cameras in these datasets, discarding different data types regarding the flight (e.g., time, location, internal sensors). In this work, we propose a multi-purpose…
German Aerospace Center (DLR), Institute of Space Research（德国航空航天中心（DLR）空间研究所）；Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente（代尔夫特理工大学地理信息科学与地球观测学院）
ETH Zurich（苏黎世联邦理工学院）；Mantis Technologies（曼蒂斯科技公司）
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude. In each spectrum, a customized RetinaNet network with ResNet backbone provides human detections which are subsequently fused to minimize the overall false detection rate. We show that by optimizing the bounding box anchors and augmenting the image resolution the number of missed detections from high altitudes can be decreased by over 20 percent. Our proposed network is compare…
Temporal Point-Supervised Signal Reconstruction: A Human-Annotation-Free Framework for Weak Moving Target Detection
Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences（电子信息技术空间系统重点实验室，国家空间科学中心，中国科学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks
College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；College of Artificial Intelligence and Automation, Hohai University（河海大学人工智能与自动化学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Object detection from Unmanned Aerial Vehicles (UAVs) is of great importance in many aerial vision-based applications. Despite the great success of generic object detection methods, a significant performance drop is observed when applied to images captured by UAVs. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and different capture times. These variations lead to domain imbalances and, thus, trained models suffering from domain bias. We demonstrate that domain knowledge is a valuable source of information and thus propose…
DetReIDX: A Stress-Test Dataset for Real-World UAV-Based Person Recognition
Instituto de Telecomunicações（电信研究所）；University of Beira Interior（贝拉内尔大学）；J.N.N. College of Engineering（J.N.N. 工程学院）；School of Computational Sciences（计算科学学院）；SRTM University（SRTM 大学）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；SRM Institute of Science and Technology（SRM 科学与技术研究所）
