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SmartPhoto: A Resource-Aware Crowdsourcing Approach
for Image Sensing with Smartphones
Yi Wang, Wenjie Hu, Yibo Wu and Guohong CaoThe Pennsylvania State University
Photo Crowdsourcing Enabled by the popularity of smartphones
Equipped with cameras, sensors and network interfaces People are willing to share photos
The success of Flickr and Instagram A number of promising applications
Grassroots journalism, photo tourism, disaster recovery
Motivating Examples Post-earthquake recovery
First responders survey the area by taking photos Damaged/overloaded networks limit the bandwidth for
photo uploading Map service with virtual tours
Enhance user experience by showing street views Impractical to store and process billions of available photos
Key challenge: resource limitation
Outline
Introduction Photo utility model Max-utility with bandwidth constraint Achieving required utility with min-selection Testbed implementation Performance evaluation Summary
Photo Utility Model Characterize photo usefulness in a way that is both
meaningful and resource-friendly Different from traditional sensor coverage Utility: the amount of aspects a photo covers
Photo metadata Aspect coverage Coverage overlap
Outline
Introduction Photo utility model Max-utility with bandwidth constraint Achieving required utility with min-selection Testbed implementation Performance evaluation Summary
Max-Utility Problem Problem statement
With some known targets and photos, how to choose a given number of photos out of all the candidates to maximize the total utility?
Example: choose 3 out of 10 photos
Max-Utility Problem Conversion to maximum coverage problem
NP-hard!
Weighted maximum coverage problem
Max-Utility Problem Maximum coverage problem is NP-hard Greedy approximation
A multi-round selection process In each round, select the subset with the most weight
contribution to the total weight Once a subset is selected, the elements it covers are
removed from future consideration Approximation ratio
Outline
Introduction Photo utility model Max-utility with bandwidth constraint Achieving required utility with min-selection Testbed implementation Performance evaluation Summary
Min-Selection Problem Problem statement
With some known targets and photos, how to choose the minimum number of photos such that all the required intervals are covered?
Conversion to the NP-hard set cover problem Greedy approximation
In each round, select the subset with the most number of new elements
Approximation ratio
Outline
Introduction Photo utility model Max-utility with bandwidth constraint Achieving required utility with min-selection Testbed implementation Performance evaluation Summary
Metadata Acquisition Location: GPS Field-of-view: API Coverage range: depends on application; 50m as a
reference range Orientation: accelerometer, magnetic field sensor,
gyroscope
Improving Orientation Accuracy Hybrid method
Enhanced method Calibrate the result of hybrid method by an ortho-
normalization process Results
Occlusion & Out-of-Focus When the camera is focused at distance D, an object
only appears sharp if it is within range [Dnear, Dfar], where Dnear < D < Dfar
The length of the range is called depth-of-field (DOF)
Distance from camera to dictionary: 100cmLeft: Dnear =85cm, Dfar=105cmRight: Dnear =5cm, Dfar=10cm
Outline
Introduction Photo utility model Max-utility with bandwidth constraint Achieving required utility with min-selection Testbed implementation Performance evaluation Summary
Real-World Demo Max-utility vs. random selection
Performance of Max-Utility Left: our algorithm converges to the best achievable utility
much faster Right: our algorithm performs close to the best achievable
utility even when the bandwidth is heavily constrained
Performance of Min-Selection Left: our algorithm selects small number of photos to achieve
the required coverage, regardless of the increasing redundancy of related photos
Right: the increase of selected photos to cover more targets is slower for our algorithm
Summary SmartPhoto: a resource-aware framework to
optimize the selection of crowdsourced photos Photo utility model Optimization problems
Max-utility with bandwidth constraint Achieving required utility with min-selection Approximation ratio
Testbed based on Android smartphones Real-world demo and extensive simulations
Thank you!
http://mcn.cse.psu.edu
The paper and slides are also available at:http://www.cse.psu.edu/~yxw185
Online Max-Utility Problem Time is divided into transmission periods. Some new
photos are available in each period. Problem statement
Given the targets and the photos available at the beginning of each period, how to choose photos in each period such that the bandwidth constraint is satisfied and at the end of the period, all the selected photos up to now have the maximum utility
Solution Use the algorithm in max-utility problem to do greedy
selection in each period Aspects covered in previous periods are not considered
Performance of Online Max-Utility Left: for our algorithm, the utility is above 350 after t7 Right: our algorithm exploits the more number of new photos
each period to improve its result