# Users plan optimization for participatory urban texture documentation

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## Abstract

We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high quality urban texture documentation. We propose to implement PTD in two steps. At the first step, termed viewpoint selection, a minimum number of viewpoints in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) with a desirable quality can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we define each of these steps and prove that both are NP-hard problems. Accordingly, we propose efficient algorithms to implement the viewpoint selection and assignment problems. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.

## Keywords

Participatory data collection Texture documentation Sensor placement Location-based services Participation plan optimization Optimization## References

- 1.Banaei-Kashani F, Shirani-Mehr H, Pan B, Bopp N, Nocera L, Shahabi C (2010) Geosim: a geospatial data collection system for participatory urban texture documentation. Special Issue of IEEE Data Eng Bull 33(2):40–45Google Scholar
- 2.Blum A, Chawla S, Karger DR, Lane T, Meyerson A, Minkoff M (2003) Approximation algorithms for orienteering and discounted-reward tsp. In: FOCS, pp 46–55Google Scholar
- 3.Borgstrom PH, Singh A, Jordan BL, Sukhatme GS, Batalin MA, Kaiser WJ (2008) Energy based path planning for a novel cabled robotic system. In: IROS, pp 1745–1751Google Scholar
- 4.Chakravarty S, Shekhawat A (1992) Parallel and serial heuristics for the minimum set cover problem. J Supercomput 5(4):331–345CrossRefGoogle Scholar
- 5.Chao I, Golden BL, Wasil EA (1996) The team orienteering problem. Eur J Oper Res 88(3):464–474CrossRefGoogle Scholar
- 6.Chao IM, Golden BL, Wasil EA (1996) A fast and effective heuristic for the orienteering problem. Eur J Oper Res 88(3):475–489CrossRefGoogle Scholar
- 7.Chekuri C, Korula N, Pál M (2008) Improved algorithms for orienteering and related problems. In: SODA 2008. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp 661–670Google Scholar
- 8.Chekuri C, Pal M (2005) A recursive greedy algorithm for walks in directed graphs. In: FOCS 2005. IEEE Computer Society, Washington, DC, USA, pp 245–253Google Scholar
- 9.Chen K, Har-Peled S (2008) The euclidean orienteering problem revisited. SIAM J Comput 38(1):385–397CrossRefGoogle Scholar
- 10.Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms, 2nd edn. McGraw-Hill Science/Engineering/MathGoogle Scholar
- 11.Dhillon SS, Chakrabarty K (2003) Sensor placement for effective coverage and surveillance in distributed sensor networks. Wireless communications and networking, 2003. WCNC 2003, vol 3. IEEE, pp 1609–1614Google Scholar
- 12.Engel J, Pasewaldt S, Trapp M, Döllner J (2012) An immersive visualization system for virtual 3d city models. In: Proceedings of the 20th international conference on GeoInformatics. IEEE GRSSGoogle Scholar
- 13.Evans W, Kirkpatrick D, Townsend G (1997) Right triangular irregular networks. Tech. rep., Algorithmica, Tucson, AZ, USAGoogle Scholar
- 14.Fischetti M, Gonzalez JJS, Toth P (1998) Solving the orienteering problem through branch-and-cut. INFORMS J Comput 10(2):133–148CrossRefGoogle Scholar
- 15.Forsyth DA, Ponce J (2002) Computer vision: a modern approach. Prentice Hall Professional Technical ReferenceGoogle Scholar
- 16.Fowler RJ, Little JJ (1979) Automatic extraction of irregular network digital terrain models. In: SIGGRAPH ’79: proceedings of the 6th annual conference on Computer graphics and interactive techniques. ACM, New York, NY, USA, pp 199–207CrossRefGoogle Scholar
- 17.
- 18.Leachtenauer JC, Driggers RG (2001) Surveillance and reconnaissance imaging systems: modeling and performance prediction. Artech House, BostonGoogle Scholar
- 19.Guestrin C, Krause A, Singh AP (2005) Near-optimal sensor placements in gaussian processes. In: ICML 2005. ACM, New York, NY, USA, pp 265–272CrossRefGoogle Scholar
- 20.Guillou E, Meneveaux D, Maisel E, Bouatouch K (2000) Using vanishing points for camera calibration and coarse 3d reconstruction from a single image. Vis Comput 16(7):396–410CrossRefGoogle Scholar
- 21.Hecht E (2002) Optics. Addison-WesleyGoogle Scholar
- 22.Hörster E, Lienhart R (2006) On the optimal placement of multiple visual sensors. In: VSSN 2006. ACM, New York, NY, USA, pp 111–120CrossRefGoogle Scholar
- 23.Huang CF, Tseng YC (2003) The coverage problem in a wireless sensor network. In: WSNA 2003. ACM, New York, NY, USA, pp 115–121CrossRefGoogle Scholar
- 24.Krause A, Guestrin C (2009) Optimizing sensing: from water to the web. Comput 42:38–45CrossRefGoogle Scholar
- 25.Krause A, Rajagopal R, Gupta A, Guestrin C (2009) Simultaneous placement and scheduling of sensors. In: IPSN ’09: proceedings of the 2009 international conference on information processing in sensor networks. IEEE Computer Society, Washington, DC, USA, pp 181–192Google Scholar
- 26.Lee DT, Lin AK (1986) Computational complexity of art gallery problems. IEEE Trans Inf Theory 32(2):276–282CrossRefGoogle Scholar
- 27.Lee JS, Hoh B (2010) Dynamic pricing incentive for participatory sensing. J Perv Mob Comp 6:693–708CrossRefGoogle Scholar
- 28.Meguerdichian S, Koushanfar F, Potkonjak M, Srivastava MB (2001) Coverage problems in wireless ad-hoc sensor networks. In: INFOCOM, pp 1380–1387Google Scholar
- 29.Murray AT, Kim K, Davis JW, Machiraju R, Parent RE (2007) Coverage optimization to support security monitoring. Comput Environ Urban Syst 31(2):133–147CrossRefGoogle Scholar
- 30.Samet H, Sankaranarayanan J, Alborzi H (2008) Scalable network distance browsing in spatial databases. In: SIGMOD conference, pp 43–54Google Scholar
- 31.Semmo A, Trapp M, Kyprianidis JE, Döllner J (2012) Interactive visualization of generalized virtual 3d city models using level-of-abstraction transitions. ACM GIS 31(3):885–894Google Scholar
- 32.Shirani-Mehr H, Banaei-Kashani F, Shahabi C (2009) Efficient viewpoint assignment for urban texture documentation. In: GIS ’09: proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 62–71Google Scholar
- 33.Shirani-Mehr H, Banaei-Kashani F, Shahabi C (2009) Efficient viewpoint selection for urban texture documentation. In: Third international conference on geosensor networksGoogle Scholar
- 34.Singh A, Krause A, Guestrin C, Kaiser WJ (2009) Efficient informative sensing using multiple robots. J Artif Intell Res 34(1):707–755Google Scholar
- 35.Singh A, Krause A, Kaiser WJ (2009) Nonmyopic adaptive informative path planning for multiple robots. In: IJCAI’09: proceedings of the 21st international jont conference on Artifical intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 1843–1850Google Scholar
- 36.Spiegel MR (1992) Mathematical handbook of formulas and tables, 28th printing edn. McGraw HillGoogle Scholar
- 37.Tan PN, Steinbach M, Kumar V (2005) Introduction to Data Mining, 1st edn. Addison WesleyGoogle Scholar
- 38.Tsai F, Lin HC (2007) Polygon-based texture mapping for cyber city 3d building models. Int J Geogr Inf Sci 21(9):965–981CrossRefGoogle Scholar
- 39.Vansteenwegen P, Souffriau W, Berghe GV, Oudheusden DV (2009) A guided local search metaheuristic for the team orienteering problem. Eur J Oper Res 196(1):118–127CrossRefGoogle Scholar
- 40.Wolff RW (1990) A note on pasta and anti-pasta for continuous-time markov chains. Oper Res 38(1):176–177CrossRefGoogle Scholar
- 41.Wu CH, Lee KC, Chung YC (2007) A delaunay triangulation based method for wireless sensor network deployment. Comput Commun 30(14–15):2744–2752CrossRefGoogle Scholar
- 42.Zhang B, Sukhatme GS (2008) Adaptive sampling with multiple mobile robots. In: IEEE international conference on robotics and automationGoogle Scholar