In this paper we propose a method for improving pedestrian detection in the thermal domain using two stages: first, a generative data augmentation approach is used, then a domain adaptation method using generated data adapts an RGB pedestrian detector. Our model, based on the Least-Squares Generative Adversarial Network, is trained to synthesize realistic thermal versions of input RGB images which are then used to augment the limited amount of labeled thermal pedestrian images available for training. We apply our generative data augmentation strategy in order to adapt a pretrained YOLOv3 pedestrian detector to detection in the thermal-only domain. Experimental results demonstrate the effectiveness of our approach: using less than 50\% of available real thermal training data, and relying on synthesized data generated by our model in the domain adaptation phase, our detector achieves state-of-the-art results on the KAIST Multispectral Pedestrian Detection Benchmark; even if more real thermal data is available adding GAN generated images to the training data results in improved performance, thus showing that these images act as an effective form of data augmentation. To the best of our knowledge, our detector achieves the best single-modality detection results on KAIST with respect to the state-of-the-art.
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）
Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialM…
Inferring the full transportation network, including sidewalks and cycleways, is crucial for many automated systems, including autonomous driving, multi-modal navigation, trip planning, mobility simulations, and freight management. Many transportation decisions can be informed based on an accurate pedestrian network, its interactions, and connectivity with the road networks of other modes of travel. A connected pedestrian path network is vital to transportation activities, as sidewalks and crossings connect pedestrians to other modes of transportation. However, information about these paths' l…
UBTECH Sydney Artificial Intelligence Centre, The University of Sydney（悉尼大学优必选悉尼人工智能中心）；The University of Sydney（悉尼大学）
Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentation of popular pedestrian datasets, such as Caltech and Citypersons, can be extremely challenging because real pedestrians are commonly in low quality. Due to the factors like occlusions, blurs, and low-resolution, it is significantly difficult for existing augmentation approaches, which generally synthesize data using 3D engines or generative adversarial networks (GANs), to generate realistic-looking pedestrians. Alte…
Detection of pedestrians in aerial imagery captured by drones has many applications including intersection monitoring, patrolling, and surveillance, to name a few. However, the problem is involved due to continuouslychanging camera viewpoint and object appearance as well as the need for lightweight algorithms to run on on-board embedded systems. To address this issue, the paper proposes a framework for pedestrian detection in videos based on the YOLO object detection network [6] while having a high throughput of more than 5 FPS on the Jetson TX2 embedded board. The framework exploits deep lear…
Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning
School of Civil and Environmental Engineering, University of New South Wales (UNSW)（新南威尔士大学土木与环境工程学院）；Research Centre for Integrated Transport Innovation (rCITI)（综合交通创新研究中心）
In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for a more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excita…
The low resolution of objects of interest in aerial images makes pedestrian detection and action detection extremely challenging tasks. Furthermore, using deep convolutional neural networks to process large images can be demanding in terms of computational requirements. In order to alleviate these challenges, we propose a two-step, yes and no question answering framework to find specific individuals doing one or multiple specific actions in aerial images. First, a deep object detector, Single Shot Multibox Detector (SSD), is used to generate object proposals from small aerial images. Second, a…
U.S. Army Research Laboratory（美国陆军研究实验室）；Booz Allen Hamilton（博思艾伦汉密尔顿）
Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively. We propose a new synthesis method to enhance the discriminative quality of synthesized visible face imagery by leveraging both global (e.g., entire face) and local regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an independent representation for the corresponding area, and (2) additiona…
Space Applications Center, Indian Space Research Organisation（印度空间研究组织空间应用中心）
Convolutional Neural Network (CNN) is achieving remarkable progress in various computer vision tasks. In the past few years, the remote sensing community has observed Deep Neural Network (DNN) finally taking off in several challenging fields. In this study, we propose a DNN to generate a predefined High Resolution (HR) synthetic spectral band using an ensemble of concurrent Low Resolution (LR) bands and existing HR bands. Of particular interest, the proposed network, namely DeepSWIR, synthesizes Short-Wave InfraRed (SWIR) band at 5m Ground Sampling Distance (GSD) using Green (G), Red (R) and N…
Learning Street View Representations with Spatiotemporal Contrast
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University（遥感与地理信息系统研究所，地球与空间科学学院，北京大学）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（土木与环境工程系，香港科学与技术大学）；SEAI Lab, Department of Geography and the Environment, University of Texas at Austin（SEAI实验室，地理与环境系，德克萨斯大学奥斯汀分校）
Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；Massachusetts Institute of Technology（麻省理工学院）
At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we can not disregard the most vulnerable elements in the urban landscape: pedestrians, exposed to higher risks than other road users. Indeed, safe, accessible, and sustainable transport systems in cities are a core target of the UN's 2030 Agenda. Thus, there is an opportunity to apply advanced computational tools to the problem of traffic safety, in regards especially to pedestrians, who have been often overlooked in the past. T…
So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
University of Liverpool, UK（利物浦大学）；Nanyang Technological University（南洋理工大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Sheffield（谢菲尔德大学）
TU Delft（代尔夫特理工大学）；TU Eindhoven（埃因霍温理工大学）；Cyclomedia B.V（Cyclomedia公司）
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pedestrians, vehicles) in street-view imagery. We propose a novel moving object segmentation algorithm exploiting consistencies in depth across multiple street-view images that are later combined with the results of a segmentation network. The detected moving objects are removed and inpainted with information from other views, to obtain a realistic out…
Johns Hopkins University（约翰霍普金斯大学）；University of Nebraska（内布拉斯加大学）；U.S. Army DEVCOM Army Research Laboratory (ARL)（美国陆军DEVCOM陆军研究实验室）；Booz Allen Hamilton（博思艾伦汉密尔顿公司）
Thermal-to-visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or learn domain-invariant robust features from these modalities for cross-modal matching. In this paper, we use attributes extracted from visible images to synthesize attribute-preserved visible images from thermal imagery for cross-modal matching. A pre-trained attribute predictor network is used to extract the attributes from the visible image. Then, a novel multi-scale generator is proposed…
Correctly identifying crosswalks is an essential task for the driving activity and mobility autonomy. Many crosswalk classification, detection and localization systems have been proposed in the literature over the years. These systems use different perspectives to tackle the crosswalk classification problem: satellite imagery, cockpit view (from the top of a car or behind the windshield), and pedestrian perspective. Most of the works in the literature are designed and evaluated using small and local datasets, i.e. datasets that present low diversity. Scaling to large datasets imposes a challen…
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Inria（法国国家信息与自动化研究所）；Munich University of Applied Sciences（慕尼黑应用科学大学）；the University of Tokyo（东京大学）
We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process increases the quality of the generated images and exerts more control on their characteristics. Spatially adaptive normalization layers fuse both inputs and are applied to a full-bl…
Nagoya University（名古屋大学）；Chubu University（中部大学）；Tokyo Institute of Technology（东京工业大学）；Advanced Industrial Science and Technology（产业技术综合研究所）
In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds makes it unstable to monitor the situation on the ground with the visible light camera. Images captured by a longer wavelength are introduced to reduce…
New York University（纽约大学）；Rutgers University（罗格斯大学）；University of São Paulo（圣保罗大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Large-scale analysis of pedestrian infrastructures, particularly sidewalks, is critical to human-centric urban planning and design. Benefiting from the rich data set of planimetric features and high-resolution orthoimages provided through the New York City Open Data portal, we train a computer vision model to detect sidewalks, roads, and buildings from remote-sensing imagery and achieve 83% mIoU over held-out test set. We apply shape analysis techniques to study different attributes of the extracted sidewalks. More specifically, we do a tile-wise analysis of the width, angle, and curvature of …
OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China（浙江大学计算机科学与技术学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China（浙江大学杭州全球科技创新中心）；School of Software Technology, Zhejiang University, Ningbo 315048, China（浙江大学软件学院）；Ocean College, Zhejiang University, Zhoushan 316021, China（浙江大学海洋学院）；State Key Laboratory of Ocean Sensing, Hangzhou 311200, China（杭州海洋传感国家重点实验室）；Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan 316021, China（舟山海洋研究中心）
UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images
Heinz College of Information Systems and Public Policy（信息系统与公共政策学院）；Carnegie Mellon University（卡内基梅隆大学）；Institute of Remote Sensing and Geographical Information System（遥感与地理信息系统研究所）
University of Tehran（德黑兰大学）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）
Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused b…
We introduce Seamless Satellite-image Synthesis (SSS), a novel neural architecture to create scale-and-space continuous satellite textures from cartographic data. While 2D map data is cheap and easily synthesized, accurate satellite imagery is expensive and often unavailable or out of date. Our approach generates seamless textures over arbitrarily large spatial extents which are consistent through scale-space. To overcome tile size limitations in image-to-image translation approaches, SSS learns to remove seams between tiled images in a semantically meaningful manner. Scale-space continuity is…
Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class …
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）；Department of Informatics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院信息学系）；Department of Computer Science, Technical University of Munich (TUM)（慕尼黑工业大学计算机科学系）
A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；University of Missouri（密苏里大学）；Computer Science University of Missouri Columbia, MO, USA（计算机科学密苏里大学哥伦比亚分校）
Baidu Research（百度研究院）；Deepwise AI Lab（深度智谷人工智能实验室）；Zhejiang University（浙江大学）；University of Hong Kong（香港大学）；Beihang University（北京航空航天大学）；University of Maryland（马里兰大学）
Simulation systems have become an essential component in the development and validation of autonomous driving technologies. The prevailing state-of-the-art approach for simulation is to use game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (e.g., the assets for simulation) remains a manual task that can be costly and time-consuming. In addition, the fidelity of CG images still lacks the richness and authenticity of real-world images and using these images for training leads to degraded performance. In this…
Self-supervised Multi-task Learning Framework for Safety and Health-Oriented Connected Driving Environment Perception using Onboard Camera
The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）
The Ohio State University（俄亥俄州立大学）；Chongqing Changan Automobile Co Ltd（重庆长安汽车股份有限公司）
Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorithms and conducting human driver experiments. Low visual fidelity breaks immersion for human-in-the-loop driving experiments. Conventional computer graphics pipelines use detailed 3D models, meshes, textures, and rendering engines to generate 2D images from 3D scenes. These processes are labor-intensive, and they do not generate photorealistic imagery. Here we introduce a hybrid generative neural graphics pipeline for …
One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches have enabled great advances in using camera imagery to detect and classify objects. But for a safety critical application, such as autonomous driving, the error rates of the current state of the art are still too high to enable safe operation. Moreover, the characterization of object detector performance is primarily limited to testing on prerecorded datasets. Errors that occur on novel data go undetected without add…
Diverse Instance Generation via Diffusion Models for Enhanced Few-Shot Object Detection in Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
