PicPick: a generic data selection framework for mobile crowd photography

Guo, Bin; Chen, Huihui; Yu, Zhiwen; Xie, Xing; Zhang, Daqing

doi:10.1007/s00779-016-0924-x

Cited by 12 publications

(22 citation statements)

References 26 publications

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“…Image-based 3D reconstruction requires to collect sufficient and high quality fresh photos that best cover the reconstruction target area. Photo crowdsourcing [7] (also known as mobile crowd photography [12], [13]) can be regarded as an ideal tool for photo collection, which requires large amounts of participants to contribute their photos via rich builtin sensors of their hand-held devices. To guarantee good performance of photo crowdsourcing results, it is essential to select a proper set of photos.…”

Section: Photo Crowdsourcingmentioning

confidence: 99%

“…Thus, omnidirectional 3D models can be reconstructed in real-time. Compared with the traditional (e.g., centralized cloud based) photo crowdsourcing schemes (e.g., [7], [12], [13], [14]), we focus on both real-time photo transmission on the network side and the selection of high quality fresh photos on the mobile user side, in order to realize real-time 3D reconstruction. Indeed, our proposal also has the follow-ing benefits: i) real-time data aggregation and processing, since edge server is close to data sources, ii) reducing privacy leakage risks, since sensitive information is processed locally at edge servers, and iii) enabling location-aware realtime 3D services and applications.…”

Section: Edge Computingmentioning

confidence: 99%

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An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Chen

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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Image-based three-dimensional (3D) reconstruction utilizes a set of photos to build 3D model and can be widely used in many emerging applications such as augmented reality (AR) and disaster recovery. Most of existing 3D reconstruction methods require a mobile user to walk around the target area and reconstruct objectives with a hand-held camera, which is inefficient and time-consuming. To meet the requirements of delay intensive and resource hungry applications in 5G, we propose an edge computing-based photo crowdsourcing (EC-PCS) framework in this paper. The main objective is to collect a set of representative photos from ubiquitous mobile and Internet of Things (IoT) devices at the network edge for real-time 3D model reconstruction, with network resource and monetary cost considerations. Specifically, we first propose a photo pricing mechanism by jointly considering their freshness, resolution and data size. Then, we design a novel photo selection scheme to dynamically select a set of photos with the required target coverage and the minimum monetary cost. We prove the NP-hardness of such problem, and develop an efficient greedy-based approximation algorithm to obtain a near-optimal solution. Moreover, an optimal network resource allocation scheme is presented, in order to minimize the maximum uploading delay of the selected photos to the edge server. Finally, a 3D reconstruction algorithm and a 3D model caching scheme are performed by the edge server in real time. Extensive experimental results based on real-world datasets demonstrate the superior performance of our EC-PCS system over the existing mechanisms. Index Terms-three-dimensional (3D) reconstruction, multi-access edge computing, photo crowdsourcing.

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Section: Photo Crowdsourcingmentioning

confidence: 99%

Section: Edge Computingmentioning

confidence: 99%

An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Chen

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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“…As a result, VCS has broadened the range of mobile crowdsensing applications. Flexsensing [ 2 ], LuckyPhoto [ 3 ], SmartPhoto [ 4 ], Picpick [ 5 ], FlierMeet [ 6 ], and CrowdTracking [ 7 ] are examples of previous projects that demonstrate that VCS is useful and, in many cases, superior to traditional visual sensing solutions that rely on the deployment of stationary cameras for monitoring.…”

Section: Introductionmentioning

confidence: 99%

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments

Mowafi

Awad

Al-Quran

2022

Sensors

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Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers’ interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.

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“…This data selection problem is a key research problem in VCS. In the literature, there have been many existing studies in this area such as [3], [4], [5]. In these works, both context and content features have been considered in similarity and redundancy measurements for data selection.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Selection With Context and Content Features for Visual Crowdsensing

Song

2022

IEEE Open J. Veh. Technol.

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Visual crowdsensing (VCS) is becoming predominant in mobile crowdsensing, but there still exist various unique challenges, including large sizes of visual data, multidimensional requirements, and intensive processing demands. As a key research problem in VCS, data selection filters out redundant data and only retains most representative samples, which can effectively reduce the complexity and cost for VCS. In this paper, we study a phase-by-phase data selection approach, in which metadata are first used to pre-select collected photos and then only selected ones are sent to a backend server for further processing based on content features. As such, the initial selection can be completed on nearby edge servers in mobile edge computing (MEC), while more intensive content processing can be done in a remote cloud. We evaluate different initial data selection algorithms using traditional performance measures as well as adapted clustering indices as quality metrics. Moreover, we formulate an integer linear program (ILP) problem for the final data selection based on the scale-invariant feature transform (SIFT) feature. This content-based selection can complement the initial data selection based on contextual metadata. The simulation results show the differences of these selection algorithms and provide guidance on how to choose an appropriate one according to application needs.INDEX TERMS Mobile crowdsensing, visual crowdsensing, edge computing, data selection, hierarchical clustering, maximum coverage problem.

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PicPick: a generic data selection framework for mobile crowd photography

Cited by 12 publications

References 26 publications

An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments

Hybrid Data Selection With Context and Content Features for Visual Crowdsensing

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