Dynamic Traffic Modeling From Overhead Imagery

Workman, Scott; Jacobs, Nathan

doi:10.1109/cvpr42600.2020.01233

Cited by 14 publications

(5 citation statements)

References 28 publications

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“…INTRODUCTION There is a growing need for automated object localization and identification systems for overhead imagery for traffic control, national parks, wilderness areas, natural disaster surveillance, agriculture, maritime piracy, etc. Research efforts are underway in precision agriculture [3], emergency rescue [4], terrestrial and naval traffic monitoring [5], and industrial surveillance [6] to integrate accurate automated object localization and identification in overhead systems. The challenge lies in the fact that due to high ground sample distance (GSD), the aerial imagery content varies significantly within the same area of capture or drone flight.…”

Section: Domain Adaptation With Contrastive Learning For Object Detec...mentioning

confidence: 99%

Domain Adaptation with Contrastive Learning for Object Detection in Satellite Imagery

Biswas,

Tesic

2023

Preprint

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<p>State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestrial region captured and the high variability of acquisition conditions. Another reason is that the number and size of objects in aerial imagery are very different than in the consumer data. In this work, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heatmap-based region proposal network, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global components. We show we can alleviate the degradation of object identification in previously unseen datasets. We create a first-ever domain adaptation benchmark using contrastive learning for the object detection task in highly imbalanced satellite datasets with significant domain gaps and dominant small objects from existing satellite benchmarks—the proposed method results in up to a 7.4% increase in mAP performance measure over the best state-of-art. </p>

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Section: Domain Adaptation With Contrastive Learning For Object Detec...mentioning

confidence: 99%

Domain Adaptation with Contrastive Learning for Object Detection in Satellite Imagery

Biswas,

Tesic

2023

Preprint

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“…Most previous work has either ignored time, or merely used it to filter images outside of a time interval prior to spatial smoothing. Our work is similar to [37], but we focus on mapping visual attributes.…”

Section: Image-driven Mappingmentioning

confidence: 99%

Learning a Dynamic Map of Visual Appearance

Salem

Workman

Jacobs

2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including imagedriven mapping, image geolocalization, and metadata verification.

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“…YOLO along with road pixel segmentation [3] is used in some methods [43] for vehicle detection and tracking. Workman et al [52] use overhead imagery to understand patterns in traffic flow. Open-source software [5,13,16,37] facilitates the process of training object detectors on custom data sets or using pre-trained models on common datasets [19,41].…”

Section: A Detecting People and Vehicles In Imagesmentioning

confidence: 99%

Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

Ghodgaonkar,

Chakraborty,

Banna

et al. 2020

Preprint

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In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods such as inperson reporting is dangerous because observers may risk infection. A better solution is to observe activities through network cameras; this approach is scalable and observers can stay in safe locations. This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called "lockdown"). We discover 11,140 network cameras that provide real-time data and we present the results across 15 countries. We collect data from these cameras beginning April 2020 at approximately 0.5TB per week. After analyzing 10,424,459 images from still image cameras and frames extracted periodically from video, the data reveals that the residents in some countries exhibited more activity (judged by numbers of people and vehicles) after the restrictions were lifted. In other countries, the amounts of activities showed no obvious changes during the restrictions and after the restrictions were lifted. The data further reveals whether people stay "social distancing", at least 6 feet apart. This study discerns whether social distancing is being followed in several types of locations and geographical locations worldwide and serve as an early indicator whether another wave of infections is likely to occur soon.

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Dynamic Traffic Modeling From Overhead Imagery

Cited by 14 publications

References 28 publications

Domain Adaptation with Contrastive Learning for Object Detection in Satellite Imagery

Domain Adaptation with Contrastive Learning for Object Detection in Satellite Imagery

Learning a Dynamic Map of Visual Appearance

Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

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