Abstract
Excerpted from "Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices" from Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems with permission. http://dx.doi.org/10.1145/2809695.2809711 © ACM 2015.
Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers object recognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing.
- G. Takacs, Y. Xiong, R. Grzeszczuk, V. Chandrasekhar, W. chao Chen, K. Pulli, N. Gelfand, T. Bismpigiannis, and B. Girod. Outdoors augmented reality on mobile phone using loxelbased visual feature organization. In MIR, 2008. Google ScholarDigital Library
- R. Azuma. A survey of augmented reality, 1997.Google Scholar
- R. Klette, J. Ahn, R. Haeusler, S. Herman, J. Huang, W. Khan, S. Manoharan, S. Morales, J. Morris, R. Nicolescu, F. Ren, K. Schauwecker, and X. Yang. Advance in vision-based driver assistance. In ICETCE, 2011. Google ScholarCross Ref
- E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl. MAUI: Making smartphones last longer with code offload. In MobiSys, 2010. Google ScholarDigital Library
- B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In IJCAI, 1981. Google ScholarDigital Library
- M. Dantone, L. Bossard, T. Quack, and L. Van Gool. Augmented faces. In ICCVW, 2011. Google ScholarCross Ref
- C.-W. Ngo, Y.-F. Ma, and H.-J. Zhang. Video summarization and scene detection by graph modeling. IEEE Trans. Cir. and Sys. for Video Technol., 2005. Google ScholarDigital Library
- Y. Zhuang, Y. Rui, T. Huang, and S. Mehrotra. Adaptive key frame extraction using unsupervised clustering. In ICIP, 1998.Google Scholar
- M. Cooper and J. Foote. Discriminative techniques for keyframe selection. In ICME, 2005. Google ScholarCross Ref
- B. Fitzpatrick. Distributed caching with memcached. In Linux J. 2004. Google ScholarDigital Library
- E. Nygren, R. K. Sitaraman, and J. Sun. The Akamai network: a platform for high-performance internet applications. In SIGOPS, 2010. Google ScholarDigital Library
- A. J. Smith. Cache Memories. In ACM CSUR, 1982. Google ScholarDigital Library
- K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan. Towards wearable cognitive assistance. In MobiSys, 2014. Google ScholarDigital Library
- R. Newton, S. Toledo, L. Girod, H. Balakrishnan, and S. Madden. Wishbone: Profile-based Partitioning for, Sensornet Applications. In NSDI, 2009. Google ScholarDigital Library
- M.-R. Ra, A. Sheth, L. Mummert, P. Pillai, D. Wetherall, and R. Govindan. Odessa: Enabling interactive perception applications on mobile devices. In MobiSys, 2011. Google ScholarDigital Library
Index Terms
- GLIMPSE: Continuous, Real-Time Object Recognition on Mobile Devices
Recommendations
Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices
SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor SystemsGlimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the ...
Glimpse: A Programmable Early-Discard Camera Architecture for Continuous Mobile Vision
MobiSys '17: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and ServicesWe consider the problem of continuous computer-vision based analysis of video streams from mobile cameras over extended periods. Given high computational demands, general visual processing must currently be offloaded to the cloud. To reduce mobile ...
Comments