Real-Time Logo Recognition from Live Video Streams Using an Elastic Cloud Platform

  • Jianbing Ding
  • Hongyang Chao
  • Mansheng Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9659)


Real-time logo recognition from a live video stream has promising commercial applications. For example, a sports video website broadcasting a live soccer match could show advertisements of brands when their logos appear in the video. Although logo recognition is a well-studied problem, the vast majority of previous work focuses on recognition accuracy, rather than system efficiency. Consequently, existing methods cannot recognize logos in real-time, especially when a large number of logos appear in the video. Motivated by this, we propose a general framework that converts an offline logo detection method to a real-time one, by utilizing the massive parallel processing capabilities of an elastic cloud platform. The main challenge is to obtain high scalability, meaning that logo recognition efficiency keeps improving as we add more computing resources, as well as elasticity, meaning that the resource allocation is guided by the current workload rather than the peak load. The proposed framework achieves these by balancing workload, elastically provisioning resources, minimizing communication overhead, and eliminating performance bottlenecks in the system. Experiments using real data demonstrate the high efficiency, scalability and elasticity of the proposed solution.


Real-time streams Logo detection Elastic cloud platform 



This work is supported by the NSF of China under Grant 61173081, and the Guangzhou Science and Technology Program, China, under Grant 201510010165.


  1. 1.
  2. 2.
    Ding, J., Zhang, Z., Ma, R., Yang, Y.: Auction-based cloud service differentiation with service level objectives. Elsevier Comput. Netw. 94, 231–249 (2016)CrossRefGoogle Scholar
  3. 3.
    Fu, T., Ding, J., Ma, R., Winslett, M., Yang, Y., Zhang, Z.: DRS: dynamic resource scheduling for real-time analytics over fast streams. In: Proceedings of the IEEE ICDCS (2015)Google Scholar
  4. 4.
    Fu, T., Ding, J., Ma, R., Winslett, M., Yang, Y., Zhang, Z., Pei, Y., Ni, B.: LiveTraj: real-time trajectory tracking over live video streams. In: Proceedings of the ACM MM (2015). video programGoogle Scholar
  5. 5.
    Fernandez, R.C., Migliavacca, M., Kalyvianaki, E., Pietzuch, P.: Integrating scale out and fault tolerance in stream processing using operator state management. In: Proceedings of the ACM SIGMOD (2013)Google Scholar
  6. 6.
    Gulisano, V., Jiménez-Peris, R., Patiño-Martínez, M., Soriente, C., Valduriez, P.: StreamCloud: an elastic and scalable data stream system. IEEE Trans. Parallel Distrib. Syst. 23(12), 2351–2365 (2012)CrossRefGoogle Scholar
  7. 7.
    Heinze, T., Jerzak, Z., Hackenbroich, G., Fetzer, C.: Latency-aware elastic scaling for distributed data stream processing systems. In: Proceedings of the ACM DEBS (2014)Google Scholar
  8. 8.
    Lampert, C., Blaschko, M., Hofmann, T.: Efficient subwindow search: a branch and bound framework for object localization. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2129–2142 (2009)CrossRefGoogle Scholar
  9. 9.
    Lindeberg, T.: Scale invariant feature transform. Scholarpedia 7(5), 10491 (2012)CrossRefGoogle Scholar
  10. 10.
    Li, Y., Wan, K. Yan, X., Xu, C.: Real-time advertisement insertion in baseball video based on advertisement effect. In: Proceedings of the ACM Multimedia (2005)Google Scholar
  11. 11.
    Neumeyer, L., Robbins, B., Nair, A., Kesari, A.: S4: distributed stream computing platform. In: Proceedings of the IEEE ICDM KDCloud Workshop (2010)Google Scholar
  12. 12.
    Schneider, S., Andrade, H., Gedik, B., Biem, A., Wu, K.-L.: Elastic scaling of data parallel operators in stream processing. In: Proceedings of the IEEE IPDPS (2009)Google Scholar
  13. 13.
    Sinha, S., Frahm, J., Pollefeys, M., Genc, Y.: GPU-based video feature tracking and matching. In: Proceedings of the EDGE (2006)Google Scholar
  14. 14.
    Tan, T., Ma, R., Winslett, M., Yang, Y., Yu, Y., Zhang, Z.: Resa: realtime elastic streaming analytics in the cloud. In: Proceedings of the ACM SIGMOD (2013). undergraduate posterGoogle Scholar
  15. 15.
    Toshniwal, A., Taneja, S., Shukla, A., Ramasamy, K., Patel, J.M., Kulkarni, S., Jackson, J., Gade, K., Fu, M., Donham, J., et al.: Storm@Twitter. In: Proceedings of the ACM SIGMOD (2014)Google Scholar
  16. 16.
    Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: Proceedings of the IEEE CVPR (2009)Google Scholar
  17. 17.
    Xie, H., Gao, K., Zhang, Y., Tang, S., Li, J., Liu, Y.: Efficient feature detection and effective post-verification for large scale near-duplicate image search. IEEE Trans. Multimedia 13(6), 1319–1332 (2011)CrossRefGoogle Scholar
  18. 18.
    Yang, M., Ma, R.: Smooth task migration in Apache storm. In: Proceedings of the ACM SIGMOD (2015). undergraduate posterGoogle Scholar
  19. 19.
    Zaharia, M., Das, T., Li, H., Shenker, S., Stoica, I.: Discretize streams: an efficient and fault-tolerant model for stream processing on large clusters. In: Proceedings of the ACM SOSP (2013)Google Scholar
  20. 20.
    Zhang, Z., Ma, R., Ding, J., Yang, Y.: ABACUS: an auction-based approach to cloud service differentiation. In: Proceedings of the IEEE IC2E (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-Sen UniversityGuangzhouChina
  2. 2.SYSU-CMU Shunde International Joint Research InstituteFoshanChina
  3. 3.School of ComputingNational University of SingaporeSingaporeSingapore

Personalised recommendations