1,799 research outputs found
Layered Interpretation of Street View Images
We propose a layered street view model to encode both depth and semantic
information on street view images for autonomous driving. Recently, stixels,
stix-mantics, and tiered scene labeling methods have been proposed to model
street view images. We propose a 4-layer street view model, a compact
representation over the recently proposed stix-mantics model. Our layers encode
semantic classes like ground, pedestrians, vehicles, buildings, and sky in
addition to the depths. The only input to our algorithm is a pair of stereo
images. We use a deep neural network to extract the appearance features for
semantic classes. We use a simple and an efficient inference algorithm to
jointly estimate both semantic classes and layered depth values. Our method
outperforms other competing approaches in Daimler urban scene segmentation
dataset. Our algorithm is massively parallelizable, allowing a GPU
implementation with a processing speed about 9 fps.Comment: The paper will be presented in the 2015 Robotics: Science and Systems
Conference (RSS
DeepNav: Learning to Navigate Large Cities
We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for
navigating large cities using locally visible street-view images. The DeepNav
agent learns to reach its destination quickly by making the correct navigation
decisions at intersections. We collect a large-scale dataset of street-view
images organized in a graph where nodes are connected by roads. This dataset
contains 10 city graphs and more than 1 million street-view images. We propose
3 supervised learning approaches for the navigation task and show how A* search
in the city graph can be used to generate supervision for the learning. Our
annotation process is fully automated using publicly available mapping services
and requires no human input. We evaluate the proposed DeepNav models on 4
held-out cities for navigating to 5 different types of destinations. Our
algorithms outperform previous work that uses hand-crafted features and Support
Vector Regression (SVR)[19].Comment: CVPR 2017 camera ready versio
Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery
Street-view imagery provides us with novel experiences to explore different
places remotely. Carefully calibrated street-view images (e.g. Google Street
View) can be used for different downstream tasks, e.g. navigation, map features
extraction. As personal high-quality cameras have become much more affordable
and portable, an enormous amount of crowdsourced street-view images are
uploaded to the internet, but commonly with missing or noisy sensor
information. To prepare this hidden treasure for "ready-to-use" status,
determining missing location information and camera orientation angles are two
equally important tasks. Recent methods have achieved high performance on
geo-localization of street-view images by cross-view matching with a pool of
geo-referenced satellite imagery. However, most of the existing works focus
more on geo-localization than estimating the image orientation. In this work,
we re-state the importance of finding fine-grained orientation for street-view
images, formally define the problem and provide a set of evaluation metrics to
assess the quality of the orientation estimation. We propose two methods to
improve the granularity of the orientation estimation, achieving 82.4% and
72.3% accuracy for images with estimated angle errors below 2 degrees for CVUSA
and CVACT datasets, corresponding to 34.9% and 28.2% absolute improvement
compared to previous works. Integrating fine-grained orientation estimation in
training also improves the performance on geo-localization, giving top 1 recall
95.5%/85.5% and 86.8%/80.4% for orientation known/unknown tests on the two
datasets.Comment: This paper has been accepted by ACM Multimedia 2022. The version
contains additional supplementary material
Using Superpixels for Road Segmentation in Street View Images
Google Street View is a useful database that houses a large amount of information. This information, however, is unlabelled. We explore the use of superpixel methods for segmentation of images in this database, specifically road segmentation
Integrating aerial and street view images for urban land use classification
Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances
City-Wide Perceptions of Neighbourhood Quality using Street View Images
The interactions of individuals with city neighbourhoods is determined, in
part, by the perceived quality of urban environments. Perceived neighbourhood
quality is a core component of urban vitality, influencing social cohesion,
sense of community, safety, activity and mental health of residents.
Large-scale assessment of perceptions of neighbourhood quality was pioneered by
the Place Pulse projects. Researchers demonstrated the efficacy of
crowd-sourcing perception ratings of image pairs across 56 cities and training
a model to predict perceptions from street-view images. Variation across cities
may limit Place Pulse's usefulness for assessing within-city perceptions. In
this paper, we set forth a protocol for city-specific dataset collection for
the perception: 'On which street would you prefer to walk?'. This paper
describes our methodology, based in London, including collection of images and
ratings, web development, model training and mapping. Assessment of within-city
perceptions of neighbourhoods can identify inequities, inform planning
priorities, and identify temporal dynamics. Code available:
https://emilymuller1991.github.io/urban-perceptions/
DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGES
DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGE
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