Capturing Candid Moments using Daily Device without Dedicated Human Resource | Paulus | Fountain of Informatics Journal
Yan Sen Paulus
ORCID iD	https://www.researchgate.net/profile/Oscar_Karnalim
Home > Vol 3, No 2 (2018) >	Paulus
DOI: http://dx.doi.org/10.21111/fij.v3i2.2354
In most organizations, candid moments on held events should be captured as images for administrative purposes. For instance, if an event is sponsored by third parties, it is necessary to send some images capturing moments on that event to them. However, human resources are required to capture such images, resulting additional operational cost. This paper proposes a method to capture candid moments without human intervention. Unique to this method, daily device (i.e., mobile phone) is used to replace human resource with the help of a phone holder and a dedicated mobile application. Prior capturing images, given mobile application should be installed to the mobile phone and such phone should be attached to the phone holder that is aimed at event area. Candid images can be then automatically collected by running installed mobile application. According to our evaluation, such method can replace human resource for capturing candid moments using daily device. Further, it is more efficient than video recording (i.e., an alternative to capture candid moments) in terms of used battery power and memory.
multimedia tool; mobile application; surveillance system; automation
F. Keusch and T. Yan, “Web Versus Mobile Web: An Experimental Study of Device Effects and Self-Selection Effects,” Social Science Computer Review, vol. 35, no. 6, pp. 751–769, Dec. 2017.
D. Ferreira, A. K. Dey, and V. Kostakos, “Understanding Human-Smartphone Concerns: A Study of Battery Life,” in International Conference on Pervasive Computing, 2011, pp. 19–33.
“U.S. Technology Device Ownership 2015 | Pew Research Center,” 2015. [Online]. Available: http://www.pewinternet.org/2015/10/29/technology-device-ownership-2015/. [Accessed: 21-May-2018].
“Multiple mobile device ownership worldwide 2011-2016 | Forecast,” 2017. [Online]. Available: https://www.statista.com/statistics/245501/multiple-mobile-device-ownership-worldwide/. [Accessed: 21-May-2018].
T. K. Buennemeyer, T. M. Nelson, L. M. Clagett, J. P. Dunning, R. C. Marchany, and J. G. Tront, “Mobile Device Profiling and Intrusion Detection Using Smart Batteries,” in Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 2008, pp. 296–296.
M. Pielot, K. Church, and R. de Oliveira, “An in-situ study of mobile phone notifications,” in Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services - MobileHCI ’14, 2014, pp. 233–242.
J. W. Fresen, “Embracing distance education in a blended learning model: challenges and prospects,” Distance Education, pp. 1–17, May 2018.
W.-M. Hsieh and C.-C. Tsai, “Taiwanese high school teachers’ conceptions of mobile learning,” Computers & Education, vol. 115, pp. 82–95, Dec. 2017.
H. Crompton, D. Burke, and K. H. Gregory, “The use of mobile learning in PK-12 education: A systematic review,” Computers & Education, vol. 110, pp. 51–63, Jul. 2017.
H. Crompton and D. Burke, “The use of mobile learning in higher education: A systematic review,” Computers & Education, vol. 123, pp. 53–64, Aug. 2018.
D. H. Griauzde, J. T. Kullgren, B. Liestenfeltz, C. Richardson, and M. Heisler, “A mobile phone-based program to promote healthy behaviors among adults with prediabetes: study protocol for a pilot randomized controlled trial,” Pilot and Feasibility Studies, vol. 4, no. 1, p. 48, Dec. 2018.
G. O’Malley, G. Dowdall, A. Burls, I. J. Perry, and N. Curran, “Exploring the usability of a mobile app for adolescent obesity management.,” JMIR mHealth and uHealth, vol. 2, no. 2, p. e29, Jun. 2014.
V. Sandulescu, S. Andrews, D. Ellis, R. Dobrescu, and O. Martinez-Mozos, “Mobile app for stress monitoring using voice features,” in 2015 E-Health and Bioengineering Conference (EHB), 2015, pp. 1–4.
B. Liu et al., “Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions,” in Proceedings of the Twelfth Symposium on Usable Privacy and Security (soups 2016), 2016.
M. Lindorfer, M. Neugschwandtner, and C. Platzer, “MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis,” in 2015 IEEE 39th Annual Computer Software and Applications Conference, 2015, pp. 422–433.
B. Cui, W. Zhou, G. Fan, and Y. Wu, “Smart mobile APP of museum — Investigations and design for local culture protection,” in 2017 12th International Conference on Computer Science and Education (ICCSE), 2017, pp. 38–41.
D. Kieran and W. Yan, “A Framework for an Event Driven Video Surveillance System,” in 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2010, pp. 97–102.
T. Valentine and J. P. Davis, Forensic facial identification : theory and practice of identification from eyewitnesses, composites and CCTV. Wiley Blackwell, 2015.
I. S. Kim, H. S. Choi, K. M. Yi, J. Y. Choi, and S. G. Kong, “Intelligent visual surveillance — A survey,” International Journal of Control, Automation and Systems, vol. 8, no. 5, pp. 926–939, Oct. 2010.
Y. Tian, L. Brown, A. Hampapur, M. Lu, A. Senior, and C. Shu, “IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework,” Machine Vision and Applications, vol. 19, no. 5–6, pp. 315–327, Oct. 2008.
N. T. Siebel and S. J. Maybank, “The ADVISOR Visual Surveillance System,” in ECCV Workshop on Applications of Computer Vision, 2004, pp. 103–111.
D. Crow, “Biology Gone Wild,” Cell, vol. 170, no. 2, pp. 219–221, Jul. 2017.
P. Fusch, L. Ness, J. Booker, and G. Fusch, “The Ethical Implications of Plagiarism and Ghostwriting in an Open Society,” Journal of Social Change, vol. 9, no. 1, Jan. 2017.
O. Karnalim, “A Low-Level Structure-based Approach for Detecting Source Code Plagiarism,” IAENG International Journal of Computer Science, vol. 44, no. 4, 2017.
M. Ayub and O. Karnalim, “Predicting outcomes in introductory programming using J48 classification,” World Transactions on Engineering and Technology Education (WTE&TE), vol. 15, no. 2, 2017.
Capturing Candid Moments using Daily Device without Dedicated Human Resource | Paulus | Fountain of Informatics Journal
Yan Sen Paulus
ORCID iD	https://www.researchgate.net/profile/Oscar_Karnalim
Home > Vol 3, No 2 (2018) >	Paulus
DOI: http://dx.doi.org/10.21111/fij.v3i2.2354
In most organizations, candid moments on held events should be captured as images for administrative purposes. For instance, if an event is sponsored by third parties, it is necessary to send some images capturing moments on that event to them. However, human resources are required to capture such images, resulting additional operational cost. This paper proposes a method to capture candid moments without human intervention. Unique to this method, daily device (i.e., mobile phone) is used to replace human resource with the help of a phone holder and a dedicated mobile application. Prior capturing images, given mobile application should be installed to the mobile phone and such phone should be attached to the phone holder that is aimed at event area. Candid images can be then automatically collected by running installed mobile application. According to our evaluation, such method can replace human resource for capturing candid moments using daily device. Further, it is more efficient than video recording (i.e., an alternative to capture candid moments) in terms of used battery power and memory.
multimedia tool; mobile application; surveillance system; automation
F. Keusch and T. Yan, “Web Versus Mobile Web: An Experimental Study of Device Effects and Self-Selection Effects,” Social Science Computer Review, vol. 35, no. 6, pp. 751–769, Dec. 2017.
D. Ferreira, A. K. Dey, and V. Kostakos, “Understanding Human-Smartphone Concerns: A Study of Battery Life,” in International Conference on Pervasive Computing, 2011, pp. 19–33.
“U.S. Technology Device Ownership 2015 | Pew Research Center,” 2015. [Online]. Available: http://www.pewinternet.org/2015/10/29/technology-device-ownership-2015/. [Accessed: 21-May-2018].
“Multiple mobile device ownership worldwide 2011-2016 | Forecast,” 2017. [Online]. Available: https://www.statista.com/statistics/245501/multiple-mobile-device-ownership-worldwide/. [Accessed: 21-May-2018].
T. K. Buennemeyer, T. M. Nelson, L. M. Clagett, J. P. Dunning, R. C. Marchany, and J. G. Tront, “Mobile Device Profiling and Intrusion Detection Using Smart Batteries,” in Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 2008, pp. 296–296.
M. Pielot, K. Church, and R. de Oliveira, “An in-situ study of mobile phone notifications,” in Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services - MobileHCI ’14, 2014, pp. 233–242.
J. W. Fresen, “Embracing distance education in a blended learning model: challenges and prospects,” Distance Education, pp. 1–17, May 2018.
W.-M. Hsieh and C.-C. Tsai, “Taiwanese high school teachers’ conceptions of mobile learning,” Computers & Education, vol. 115, pp. 82–95, Dec. 2017.
H. Crompton, D. Burke, and K. H. Gregory, “The use of mobile learning in PK-12 education: A system
atic review,” Computers & Education, vol. 110, pp. 51–63, Jul. 2017.
H. Crompton and D. Burke, “The use of mobile learning in higher education: A systematic review,” Computers & Education, vol. 123, pp. 53–64, Aug. 2018.
D. H. Griauzde, J. T. Kullgren, B. Liestenfeltz, C. Richardson, and M. Heisler, “A mobile phone-based program to promote healthy behaviors among adults with prediabetes: study protocol for a pilot randomized controlled trial,” Pilot and Feasibility Studies, vol. 4, no. 1, p. 48, Dec. 2018.
G. O’Malley, G. Dowdall, A. Burls, I. J. Perry, and N. Curran, “Exploring the usability of a mobile app for adolescent obesity management.,” JMIR mHealth and uHealth, vol. 2, no. 2, p. e29, Jun. 2014.
V. Sandulescu, S. Andrews, D. Ellis, R. Dobrescu, and O. Martinez-Mozos, “Mobile app for stress monitoring using voice features,” in 2015 E-Health and Bioengineering Conference (EHB), 2015, pp. 1–4.
B. Liu et al., “Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions,” in Proceedings of the Twelfth Symposium on Usable Privacy and Security (soups 2016), 2016.
M. Lindorfer, M. Neugschwandtner, and C. Platzer, “MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis,” in 2015 IEEE 39th Annual Computer Software and Applications Conference, 2015, pp. 422–433.
B. Cui, W. Zhou, G. Fan, and Y. Wu, “Smart mobile APP of museum — Investigations and design for local culture protection,” in 2017 12th International Conference on Computer Science and Education (ICCSE), 2017, pp. 38–41.
D. Kieran and W. Yan, “A Framework for an Event Driven Video Surveillance System,” in 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2010, pp. 97–102.
T. Valentine and J. P. Davis, Forensic facial identification : theory and practice of identification from eyewitnesses, composites and CCTV. Wiley Blackwell, 2015.
I. S. Kim, H. S. Choi, K. M. Yi, J. Y. Choi, and S. G. Kong, “Intelligent visual surveillance — A survey,” International Journal of Control, Automation and Systems, vol. 8, no. 5, pp. 926–939, Oct. 2010.
Y. Tian, L. Brown, A. Hampapur, M. Lu, A. Senior, and C. Shu, “IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework,” Machine Vision and Applications, vol. 19, no. 5–6, pp. 315–327, Oct. 2008.
N. T. Siebel and S. J. Maybank, “The ADVISOR Visual Surveillance System,” in ECCV Workshop on Applications of Computer Vision, 2004, pp. 103–111.
D. Crow, “Biology Gone Wild,” Cell, vol. 170, no. 2, pp. 219–221, Jul. 2017.
P. Fusch, L. Ness, J. Booker, and G. Fusch, “The Ethical Implications of Plagiarism and Ghostwriting in an Open Society,” Journal of Social Change, vol. 9, no. 1, Jan. 2017.
O. Karnalim, “A Low-Level Structure-based Approach for Detecting Source Code Plagiarism,” IAENG International Journal of Computer Science, vol. 44, no. 4, 2017.
M. Ayub and O. Karnalim, “Predicting outcomes in introductory programming using J48 classification,” World Transactions on Engineering and Technology Education (WTE&TE), vol. 15, no. 2, 2017.