Future Work

Demonstrating the potential for our SBRIEF descriptor on mobile devices, we believe that there is room for work to be done on other simple, mobile specific implementations of other descriptors. Although OpenCV is a popular and extensive computer vision library, its mobile implementation does not appear well suited for real- time mobile applications. These simpler descriptors could be analogous to SBRIEF, such

as a simple implementation of FREAK, BRISK and ORB. Many of these descriptors have been shown to have sampling patterns more robust than BRIEF and SBRIEF, but lack mobile specific implementations. For example, on a mobile device it may be necessary to remove much of the scale and rotation invariant calculations to achieve greater frame rates. Such option flags can be added to OpenCV, or new code could be written to achieve greater performance.

It is also worth noting, as seen in the standard videos David and Dollar, that scale and rotation invariance may not be as important on mobile devices. This could be a means by which simpler versions of these descriptors are pursued, for example, ones that do not calculate the orientation of the descriptor every frame. In fact, on mobile devices scale and rotation can be derived from different sources other than pure mathematical calculation. In works such as [31], Ramhati et al. demonstrate the ability to adjust the display of content to counteract device shake. This same principle can be used to stabilize tracking and develop heuristics for rotation and scaling invariance. In addition to this, [32] presents data for image stabilization benchmarks for use with mobile phones. Even hardware solutions such as those in [33] present means to deal with device shake and rotation outside of the actual descriptor.

Bibliography

[1] A. Savakis, M. Stump, G. Tsagkatakis, R. Melton, G. Behm and G. Sterns, "Low vision assistance using face detection and tracking on android smartphones," in Circuits and Systems (MWSCAS), 2012 IEEE 55th International Midwest Symposium on,, 2012.

[2] M. Billinghurst and A. Duenser, "Augmented Reality in the Classroom," IEEE Computer, vol. 45, no. 7, pp. 56, 63, July 2012.

[3] N. Navab, T. Blum, L. Schmalstieg, A. Okur and T. Wendler, "First Deployment of Augmented Reality in Opertating Rooms," IEEE Computer, pp. 48-55, July 2012. [4] K. M. Saipullah., A. Anuar. and N. A. I. a. Y. Soo., "MEASURING POWER

CONSUMPTION FOR IMAGE PROCESSING ON ANDROID SMARTPHONE," American Journal of Applied Sciences, vol. 9, no. 12, pp. 2052-2057, 2012.

[5] D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond and D. Schmalstieg, "Real- Time Detection and Tracking for Augmented Reality on Mobile Phones," in Visualization and Computer Graphics, IEEE Transactions on, 2010.

[6] E. Rublee, V. Rabaud, K. Konolige and G. Bradski, "ORB: an efficient alternative to SIFT and SURF," in International Conference on Computer Vision (ICCV), Barcelona, 2011.

[7] M. Calonder, V. Lepetit, M. Ozuysal, T. Trzcinski, C. Strecha and P. Fua, "BRIEF: Computing a Local Binary Descriptor Very Fast," IEEE Transactions on Pattern

Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1281, 1298, July 2012.

[8] S. Leutenegger, M. Chli and R. Y. Siegwart, "BRISK: Binary Robust invariant scalabe keypoints," in IEEE International Conference on Computer Vision (ICCV), Barcelona, 2011.

[9] A. Alahi, R. Ortiz and P. Vandergheynst, "FREAK: Fast Retina Keypoint," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, 2012.

[10] G. Bradski, "Computer Vision Face Tracking For Use in a Perceptual User Interface," Intel Technology Journal, 1998.

[11] B. Babenko, M.-H. Yang and S. Belongie, "Visual Tracking with Online Multiple Instance Learning," in IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009.

[12] Z. Kalal, K. Mikolajczyk and J. Matas, "Tracking-Learning-Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1409- 1422, 2012.

[13] D. Schreiber, "Robust template tracking with drift correction," Pattern Recognition Letters, vol. 28, no. 12, pp. 1483-1491, 2007.

[14] I. Matthews, T. Ishikawa and S. Baker, "The Template Update Problem," IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 26, no. 6, pp. 810- 815, 2004.

[15] G. Tsagkatakis and A. Savakis, "Online Distance Metric Learning for Object Tracking," Circuits and Systems for Video Technology, IEEE Transactions on, vol.

21, no. 12, pp. 1810-1821, 2011.

[16] D. S. Purkayastha and N. Singhla, "Android Optimization: A Survey," International Journal of Computer Science and Computing, vol. 2, no. 6, pp. 46-52, 2013.

[17] C. Nimodia and H. R. Deshmukh, "Android Operating System," Software Engineering, vol. 3, no. 1, pp. 10-13, 2012.

[18] Android, "Android NDK," Android, [Online]. Available: https://developer.android.com/tools/sdk/ndk/index.html. [Accessed 30 September 2013].

[19] Android, "Application Fundamentals," Android, [Online]. Available: http://developer.android.com/guide/components/fundamentals.html. [Accessed 30 Semptember 2013].

[20] Oracle, "Java Native Interface Specification--Contents," Oracle, 2004, 2010.

[Online]. Available:

http://docs.oracle.com/javase/1.5.0/docs/guide/jni/spec/jniTOC.html. [Accessed 30 September 2013].

[21] ARM, "NEON," ARM, 2014. [Online]. Available:

http://www.arm.com/products/processors/technologies/neon.php. [Accessed 10 March 2014].

[22] W. Yang and Z. Liu, "Accelerating householder bidiagonalization with ARM NEON technology," in Signal & Information Processing Association Annual Summit and Conference, Hollywood, 2012.

Technology for," in RACS'11 Proceedings of the 2011 ACM Symposium on Research in Applied Computation, 2011.

[24] Android, "Using DDMS," Android, [Online]. Available: http://developer.android.com/tools/debugging/ddms.html. [Accessed 14 Januaray 2014].

[25] G. P. Perrucci, F. H. P. Fitzek and J. Widmer, "Survey on Energy Consumption Entities on the Smartphone Platform," in Vehicular Technology Conference (VTC Spring), IEEE 73rd, Yokohama, 2011.

[26] L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao and L. Yang, "Accurate Online Power Estimation and Automatic Battery Behvavior based Power Model Generation for Smartphone," in CODES/ISSS '10 Proceedings of the eigth IEEE/AC/IFIP International Conference on Hardware/software codesign and systems synthesis, New York, 2010.

[27] M. Kim, J. Kong and S. W. Chung, "Enhancing online power estimation accuracy for smartphones," IEEE Transactions on Consumer Electronics, pp. 333-339, May 2012.

[28] A. Pathak, Y. C. Hu, M. Zhang, P. Bahl and Y. Wang, "Fine-Grained Power Modeling for Smartphones," in EuroSys '11 Proceedings of the sixth Conference on Computer Systems, Salzburg, 2011.

[29] H. Spang, "Object Tracking Using Local Binary Descriptors," RIT Department of Computer Engineering, 2014.

Elementary Features," in 11th European Conference on Computer Vison, Heraklion, Crete, Greece, 2010.

[31] A. Rahmati, C. Shepard and L. Zhong, "NoShake: Content stabilization for shaking screens of mobile devices," in IEEE International Conference on Pervasive Computing and Communications, 2009, Galveston, TX, 2009.

[32] E. M. Or and D. Pundik, "Hand Motion and Image Stabilization in Hand-held Devices," IEEE Transactions on Consumer Electronics, vol. 53, no. 4, pp. 1508- 1512, Novermber 2007.

[33] C. Chiu, P. Chao, N. Kao and F. Young, "Optimal design and experimental verification of a magnetically actuated optical image stabilization system for cameras in mobile phones," Journal of Applied Physics, vol. 103, 2008.

[34] D. G. Lowe, "Object recognition from local scale-invariant features," in The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, Kerkya, 1999.