Skip to main content
SpringerLink
Log in

Mobile Phone Based Lecture Video Capturing & Streaming Technique for Efficient Bandwidth Utilization

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Advancement in smart mobile phones and wireless networks technology have broaden capability of mobile phones rather than being limited for voice communication. Smart mobile devices are true multimedia devices proficient of directing, receiving, transforming, transmitting and presentation of data like image, audio, video, and text. These features facilitate high interactive learning system in mobile phones called m-learning. Many approaches have been proposed to improve the quality, as well as reduce the duration of learning videos due to the limited memory size of mobile phones. Moreover, the cameras used so far are high-definition standard cameras which are costly. Therefore, in this paper we propose to develop an android based mobile application which captures as well as stream lecture videos using mobile phone camera. Moreover, we have proposed a content analysis algorithm to reduce size of video frames as well as bandwidth requirement for transmission. We have evaluated our proposed algorithm for various dataset captured in different classroom scenarios. We have also tested our algorithm with the MPEG compression which achieves compression ratios more than 1,000 times to those of the MPEG compression.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig.2
Fig. 3
Fig.4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig.10
Fig. 11

Similar content being viewed by others

References

  1. Leung, C. H., & Chan, Y. Y. (2003) Mobile learning: A new paradigm in electronic learning. In Proceedings 3rd IEEE international conference on advanced technologies (pp. 76–80).

  2. Patil, S. P., Sanyal, R., & Prasad, R. (2018). Progressive streaming of video data for traffic surveillance. Wireless Personal Communications, 100(2), 283–309.

    Article  Google Scholar 

  3. Mandula, K., Meda, S. R., & Jain, D. K. (2012). Research and implementation of a mobile video streaming application for ubiquitous learning. In 2012 IEEE international conference on technology enhanced education (ICTEE) (pp. 1–6).

  4. Hartsell, T., & Yuen, S. C. Y. (2006). Video streaming in online learning. AACE Journal, 14(1), 31–43.

    Google Scholar 

  5. Wu, D., Hou, Y. T., Zhu, W., Zhang, Y. Q., & Peha, J. M. (2001). Streaming video over the Internet: Approaches and directions. IEEE Transactions on circuits and systems for video technology, 11(3), 282–300.

    Article  Google Scholar 

  6. Zhang, C., Rui, Y., Crawford, J., & He, L. W. (2008). An automated end-to- end lecture capture and broadcasting system. ACM Transactions on multimedia computing, communications, and applications (TOMM), 4(1), 1–23.

  7. Fung, C. W., & Liew, S. C. (1999). End-to-end frame-rate adaptive streaming of video data. In Proceedings IEEE international conference on multimedia computing and systems (Vol. 2, pp. 67–71).

  8. Video streaming available at: https://searchunifiedcommunications.techtarget.com/definition/streaming- video

  9. Rubio Romero, L. (2011). A dynamic adaptive HTTP streaming video service for Google Android.

  10. Jo, J., Park, K., Lee, D., & Lim, H. (2014). An integrated teaching and learning assistance system meeting requirements for smart education. Wireless personal communications, 79(4), 2453–2467.

    Article  Google Scholar 

  11. Liu, T., & Choudary, C. (2007). Scalable coding and wireless streaming of lecture videos for mobile learning. Advanced Technology for Learning, 4(2), 85–91.

    Article  Google Scholar 

  12. Chang, S. F., Zhong, D., & Kumar, R. (2001). Real-time content-based adaptive streaming of sports videos. In Proceedings IEEE workshop on content-based access of image and video libraries (CBAIVL 2001) (pp. 139–146).

  13. Schön, D., Klinger, M., Kopf, S., & Effelsberg, W. (2012). Mobile quiz-a lecture survey tool using smartphones and QR tags. International Journal of Digital Information and Wireless Communications (IJDIWC), 2(3), 231–244.

    Google Scholar 

  14. Mehta, S. M., Spanias, A., & Thiagarajan, J. J. (2010). Work in progress—An interactive web-based quiz that uses the java-DSP editor to enhance student learning experience. In 2010 IEEE frontiers in education conference (FIE) (pp. T2G-1).

  15. Ford, M., & Leinonen, T. (2009). MobilED–mobile tools and services platform for formal and informal learning. Mobile learning: Transforming the delivery of education and training, 195–214.

  16. Halawa, S., Pang, D., Cheung, N. M., & Girod, B. (2011). ClassX: an open-source interactive lecture StreamingSystem. In Proceedings of the 19th ACM international conference on multimedia (pp. 719–722).

  17. Mavlankar, A., Agrawal, P., Pang, D., Halawa, S., Cheung, N. M., & Girod, B. (2010). An interactive region-of-interest video streaming system for online lecture viewing. In 2010 18th International packet video workshop (pp. 64–71).

  18. Pang, D., Halawa, S., Cheung, N. M., & Girod, B. (2011). Mobile interactive region-of-interest video streaming with crowd-driven prefetching. In Proceeding of the 2011 international ACM workshop on Interactive multimedia on mobile and portable devices (pp. 7–12).

  19. Ambikairajah, E., Epps, J., Sheng, M., Celler, B., & Chen, P. (2005). Experiences with an electronic whiteboard teaching laboratory and tablet PC based lecture presentations [DSP courses]. In Proceedings. (ICASSP'05). IEEE international conference on acoustics, speech, and signal processing, 2005. (Vol. 5, pp. v-565).

  20. Lin, Y. T., Tsai, H. Y., Chang, C. H., & Lee, G. C. (2010). Learning-focused structuring for blackboard lecture videos. In 2010 IEEE fourth international conference on semantic computing (pp. 149–155).

  21. Liu, T., & Choudary, C. (2006). Content-adaptive wireless streaming of instructional videos. Multimedia Tools and Applications, 28(2), 157–171.

    Article  Google Scholar 

  22. Kantarcı, A. (2010). Bandwidth-effective streaming of educational medical videos. Multimedia Systems, 16(6), 381–397.

    Article  Google Scholar 

  23. Javed, O., Shafique, K., & Shah, M. (2002). A hierarchical approach to robust background subtraction using color and gradient information. In Workshop on motion and video computing, 2002. Proceedings. (pp. 22–27).

  24. Lee, G. C., Yeh, F. H., Chen, Y. J., & Chang, T. K. (2017). Robust handwriting extraction and lecture video summarization. Multimedia Tools and Applications, 76(5), 7067–7085.

    Article  Google Scholar 

  25. Imran, A. S., & Cheikh, F. A. (2011). Blackboard content classification for lecture videos. In 2011 18th IEEE international conference on image processing (pp. 2989–2992).

  26. Ejaz, N., Tariq, T. B., & Baik, S. W. (2012). Adaptive key frame extraction for video summarization using an aggregation mechanism. Journal of Visual Communication and Image Representation, 23(7), 1031–1040.

    Article  Google Scholar 

  27. Onishi, M., Izumi, M., & Fukunaga, K. (2000, September). Blackboard segmentation using video image of lecture and its applications. In Proceedings 15th international conference on pattern recognition. ICPR-2000 (Vol. 4, pp. 615–618).

  28. Avaro, O., Chou, P. A., Eleftheriadis, A., Herpel, C., Reader, C., & Signès, J. (1997). The MPEG-4 systems and description languages: A way ahead in audio visual information representation. Signal Processing: Image Communication, 9(4), 385–431.

    Google Scholar 

  29. Kota, B. U., Ahmed, S., Stone, A., Davila, K., Setlur, S., & Govindaraju, V. (2019). Summarizing Lecture Videos by Key Handwritten Content Regions. In 2019 International conference on document analysis and recognition workshops (ICDARW) (Vol. 4, pp. 13–18).

  30. Yang, X. H., Yin, F., & Liu, C. L. (2018). Online video text detection with markov decision process. In 2018 13th IAPR international workshop on document analysis systems (DAS) (pp. 103–108).

  31. Gangodkar, D., Kumar, P., & Mittal, A. (2012). Robust segmentation of moving vehicles under complex outdoor conditions. IEEE Transactions on intelligent transportation systems, 13(4), 1738–1752.

    Article  Google Scholar 

  32. Mantoro, T., Ayu, M. A., & Jatikusumo, D. (2012). Live video streaming for mobile devices: An application on android platform. In 2012 2nd international conference on uncertainty reasoning and knowledge engineering (pp. 119–122).

  33. Wowza Media Systems. (2010, February 12). Wowza streaming engine – users guide version 4. [Online]. Available: http://www.wowza.com/forums/content.php?3-quick-start-guide

  34. Lin, Y. T., Tsai, H. Y., Chang, C. H., & Lee, G. C. (2010). Learning-focused structuring for blackboard lecture videos. In 2010 IEEE fourth international conference on semantic computing (pp. 149–155). IEEE.

  35. He, L. W., & Zhang, Z. (2006). Real-time whiteboard capture and processing using a video camera for remote collaboration. IEEE Transactions on Multimedia, 9(1), 198–206.

    Article  Google Scholar 

  36. Wallick, M. N., Heck, R. M., & Gleicher, M. L. (2005). Marker and chalkboard regions. In Proceedings of mirage (pp. 223–228).

  37. Masneri, S., & Schreer, O. (2014). SVM-based video segmentation and annotation of lectures and conferences. In 2014 International conference on computer vision theory and applications (VISAPP) (Vol. 2, pp. 425–432).

  38. Ognenoski, O., Razaak, M., Martini, M. G., & Amon, P. (2013). Medical video streaming utilizing MPEG-DASH. In 2013 IEEE 15th International conference on e-health networking, applications and services (Healthcom 2013) (pp. 54–59).

  39. Ullrich, C., Shen, R., Tong, R., & Tan, X. (2010). A mobile live video learning system for large-scale learning—system design and evaluation. IEEE Transactions on Learning Technologies, 3(1), 6–17.

    Article  Google Scholar 

  40. Parsola, J., Gangodkar, D., & Mittal, A. (2019). Video segmentation techniques for instructional videos-survey. Journal of Graphic Era University, 7(2), 90–107.

    Google Scholar 

  41. Chung, K., Boutaba, R., & Hariri, S. (2014). Recent trends in digital convergence information system. Wireless Personal Communications, 79(4), 2409–2413.

    Article  Google Scholar 

  42. Parsola, J., Gangodkar, D., & Mittal, A. (2021). Automated system for road extraction and traffic volume estimation for traffic jam detection. International Journal of Computational Vision and Robotics, 11(2), 127–150.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Graphic Era Hill University, Dehradun, India

    Jyoti Parsola

  2. Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India

    Jyoti Parsola, Durgaprasad Gangodkar & Ankush Mittal

Authors
  1. Jyoti Parsola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Durgaprasad Gangodkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ankush Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jyoti Parsola.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parsola, J., Gangodkar, D. & Mittal, A. Mobile Phone Based Lecture Video Capturing & Streaming Technique for Efficient Bandwidth Utilization. Wireless Pers Commun 123, 2189–2207 (2022). https://doi.org/10.1007/s11277-021-09234-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09234-0

Keywords

Search

Navigation