An Overview+Detail Surveillance Video Player: Information-Based Adaptive Fast-Forward

  • Lele Dong
  • Qing XuEmail author
  • Shang Wu
  • Xueyan Song
  • Klaus Schoeffmann
  • Mateu Sbert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9917)


In this paper, we propose an adaptive fast playback framework where multi-features are used to support arbitrary frame-rate video playback. We introduce a Jenson noise-based difference (JSND) as a distance measure between adjacent frames for video key frame extraction, and then present an interest learning model to control the playback rate according to the user preference. The proposed “smart-skip” frame schema not only helps users navigate the video content non-uniformly for any variable playback rate, but also preserves video semantic information to avoid the omission of important events. An overview+detail video player offering users an immersive experience is implemented to browse and comprehend the video content. Experimental results show that users can quickly skim the video, understand the content, and navigate into the content of interest.


Video playback Adaptive fast-forward Interest learning model Overview+detail Frame skipping transcoding 



This work has been funded by Natural Science Foundation of China (61471261, 61179067, U1333110), and by grants TIN2013-47276-C6-1-R from Spanish Government and 2014-SGR-1232 from Catalan Government (Spain).


  1. 1.
    Al-Hajri, A., Miller, G., Fong, M., Fels, S.S.: Visualization of personal history for video navigation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1187–1196. ACM (2014)Google Scholar
  2. 2.
    Chen, C.Y., Hsu, C.T., Yeh, C.H., Chen, M.J.: Arbitrary frame skipping transcoding through spatial-temporal complexity analysis. In: TENCON 2007–2007 IEEE Region 10 Conference, pp. 1–4. IEEE (2007)Google Scholar
  3. 3.
    Cheng, K.Y., Luo, S.J., Chen, B.Y., Chu, H.H.: Smartplayer: user-centric video fast-forwarding. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 789–798. ACM (2009)Google Scholar
  4. 4.
    Christmann, O., Carbonell, N., Richir, S.: Visual search in dynamic 3d visualisations of unstructured picture collections. Interact. Comput. 22(5), 399–416 (2010)CrossRefGoogle Scholar
  5. 5.
    Glance, N.S.: Recommender system and method for generating implicit ratings based on user interactions with handheld devices, US Patent 6,947,922, 20 September 2005Google Scholar
  6. 6.
    Guo, Y., Xu, Q., Sun, S., Luo, X., Sbert, M.: Selecting video key frames based on relative entropy and the extreme studentized deviate test. Entropy 18(3), 73 (2016)CrossRefGoogle Scholar
  7. 7.
    Höferlin, B., Höferlin, M., Weiskopf, D., Heidemann, G.: Information-based adaptive fast-forward for visual surveillance. Multimedia Tools Appl. 55(1), 127–150 (2011)CrossRefGoogle Scholar
  8. 8.
    Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE (2008)Google Scholar
  9. 9.
    Jiang, J., Zhang, X.P.: A new player-enabled rapid video navigation method using temporal quantization and repeated weighted boosting search. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 64–71. IEEE (2010)Google Scholar
  10. 10.
    Jiang, J., Zhang, X.P.: A smart video player with content-based fast-forward playback. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1061–1064. ACM (2011)Google Scholar
  11. 11.
    Peker, K.A.: Method and system for playing back videos at speeds adapted to content, US Patent 7,796,860, 14 September 2010Google Scholar
  12. 12.
    Petrovic, N., Jojic, N., Huang, T.S.: Adaptive video fast forward. Multimedia Tools Appl. 26(3), 327–344 (2005)CrossRefGoogle Scholar
  13. 13.
    Ramos, G., Balakrishnan, R.: Fluid interaction techniques for the control and annotation of digital video. In: Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology, pp. 105–114. ACM (2003)Google Scholar
  14. 14.
    Schoeffmann, K., Hudelist, M.A., Huber, J.: Video interaction tools: a survey of recent work. ACM Comput. Surv. (CSUR) 48(1), 14 (2015)CrossRefGoogle Scholar
  15. 15.
    Shu, H., Chau, L.P.: Variable frame rate transcoding considering motion information [video transcoding]. In: IEEE International Symposium on Circuits and Systems, ISCAS 2005, pp. 2144–2147. IEEE (2005)Google Scholar
  16. 16.
    Vetro, A., Christopoulos, C., Sun, H.: Video transcoding architectures and techniques: an overview. IEEE Sig. Process. Mag. 20(2), 18–29 (2003)CrossRefGoogle Scholar
  17. 17.
    Xu, Q., Liu, Y., Li, X., Yang, Z., Wang, J., Sbert, M., Scopigno, R.: Browsing and exploration of video sequences: a new scheme for key frame extraction and 3d visualization using entropy based jensen divergence. Inf. Sci. 278, 736–756 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Lele Dong
    • 1
  • Qing Xu
    • 1
    Email author
  • Shang Wu
    • 1
  • Xueyan Song
    • 1
  • Klaus Schoeffmann
    • 2
  • Mateu Sbert
    • 1
    • 3
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Klagenfurt University, Institute of Information TechnologyKlagenfurtAustria
  3. 3.Graphics and Imaging LabUniversitat de GironaGironaSpain

Personalised recommendations