Street-view change detection with deconvolutional networks

Alcantarilla, Pablo F.; Stent, Simon; Ros, Germán; Arroyo, Roberto; Gherardi, Riccardo

doi:10.1007/s10514-018-9734-5

Cited by 255 publications

(219 citation statements)

References 50 publications

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“…If training data are available, supervised networks can be used to learn change detection features directly from the training data [14][15][16]. However, none of them use pretrained weights to initialize their networks, even though it has a proven value for classification and object detection tasks, especially when only a limited amount of annotated data are available [17].…”

Section: Related Workmentioning

confidence: 99%

ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles

Klomp¹,

Wouw²,

B.V.³

et al. 2019

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DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: Related Workmentioning

confidence: 99%

ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles

Klomp¹,

Wouw²,

B.V.³

et al. 2019

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“…Change detection has also been broadly studied for outdoor environments [13], [14], [15]. Structural change detection from street view images is performed in [13]. Multisensor fusion SLAM, deep deconvolution networks and fast 3D reconstruction are used to determine the changing regions between pairs of images.…”

Section: Related Workmentioning

confidence: 99%

Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection

Derner

Gómez

Hernandez

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of lifelong autonomy. In computer vision-based methods employed on mobile robots, such as localization or navigation, one of the major issues is the dynamics of the scenes. The autonomous operation of the robot may become unreliable if the changes that are common in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects on the desks and other changes make many conventional methods fail. To deal with that, we present a novel method for change detection based on the similarity of local visual features. The core idea of the algorithm is to distinguish important stable regions of the scene from the regions that are changing. To evaluate the change detection algorithm, we have designed a simple visual localization framework based on feature matching and we have performed a series of real-world localization experiments. The results have shown that the change detection method substantially improves the accuracy of the robot localization, compared to using the baseline localization method without change detection.

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“…Unless otherwise stated, we train all the networks using the Adam stochastic gradient descent method with a weightdecay factor of 5 × 10 −5 , parameters β 1 = 0.9, β 2 = 0.999, and we use DaConv blocks with d = 3 dilation factors, namely j = 2 j−1 for i ∈ {1, 2, 3}. 3 In the following sections we use the notation f i : Z 2 → R to indicate the output of the ith channel of any network under consideration. All our networks are implemented using the Caffe 4 framework and trained on a single Nvidia K40 GPU.…”

Section: Depth-aware Cnn Architectures For Robotic Perception Tasksmentioning

confidence: 99%

“…Deep models have been applied to a number of robotics tasks involving RGB inputs, e.g. monocular depth prediction [1], 3D scene layout understanding [2], change detection in large 3D maps [3] and camera relocalization [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Learning Depth-Aware Deep Representations for Robotic Perception

Porzi

Bulò

Peñate-Sánchez

et al. 2017

IEEE Robot. Autom. Lett.

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Exploiting RGB-D data by means of Convolutional Neural Networks (CNNs) is at the core of a number of robotics applications, including object detection, scene semantic segmentation and grasping. Most existing approaches, however, exploit RGB-D data by simply considering depth as an additional input channel for the network. In this paper we show that the performance of deep architectures can be boosted by introducing DaConv, a novel, general-purpose CNN block which exploits depth to learn scale-aware feature representations. We demonstrate the benefits of DaConv on a variety of robotics oriented tasks, involving affordance detection, object coordinate regression and contour detection in RGB-D images. In each of these experiments we show the potential of the proposed block and how it can be readily integrated into existing CNN architectures.

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Street-view change detection with deconvolutional networks

Cited by 255 publications

References 50 publications

ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles

ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles

Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection

Learning Depth-Aware Deep Representations for Robotic Perception

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