publication . Part of book or chapter of book . Preprint . 2018

Fast Video Shot Transition Localization with Deep Structured Models.

Tang, Shitao; Feng, Litong; Kuang, Zhangkui; Chen, Yimin; Zhang, Wei;
Open Access
  • Published: 13 Aug 2018
  • Publisher: Springer International Publishing
Abstract
Detection of video shot transition is a crucial pre-processing step in video analysis. Previous studies are restricted on detecting sudden content changes between frames through similarity measurement and multi-scale operations are widely utilized to deal with transitions of various lengths. However, localization of gradual transitions are still under-explored due to the high visual similarity between adjacent frames. Cut shot transitions are abrupt semantic breaks while gradual shot transitions contain low-level spatial-temporal patterns caused by video effects in addition to the gradual semantic breaks, e.g. dissolve. In order to address the problem, we propos...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download fromView all 2 versions
http://arxiv.org/pdf/1808.0423...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book . 2019
Provider: Crossref
27 references, page 1 of 2

1. Yuso , Y., Christmas, W.J., Kittler, J.: Video shot cut detection using adaptive thresholding. In: BMVC. (2000) 1{10 [OpenAIRE]

2. Yuan, J., Li, J., Lin, F., Zhang, B.: A uni ed shot boundary detection framework based on graph partition model. In: Proceedings of the 13th annual ACM international conference on Multimedia, ACM (2005) 539{542

3. Lu, Z.M., Shi, Y.: Fast video shot boundary detection based on svd and pattern matching. IEEE Transactions on Image processing 22 (2013) 5136{5145

4. Hassanien, A., Elgharib, M., Selim, A., Hefeeda, M., Matusik, W.: Large-scale, fast and accurate shot boundary detection through spatio-temporal convolutional neural networks. arXiv preprint arXiv:1705.03281 (2017) [OpenAIRE]

5. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision, Springer (2016) 21{37

6. Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: A formal study of shot boundary detection. IEEE transactions on circuits and systems for video technology 17 (2007) 168{186

7. Liu, Z., Gibbon, D., Zavesky, E., Shahraray, B., Ha ner, P.: At&t research at trecvid 2007. In: Proc. TRECVID Workshop. (2007) 19{26

8. Gygli, M.: Ridiculously fast shot boundary detection with fully convolutional neural networks. arXiv preprint arXiv:1705.08214 (2017)

9. Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, IEEE (2015) 4353{4361 [OpenAIRE]

10. Wang, J., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y., et al.: Learning ne-grained image similarity with deep ranking. arXiv preprint arXiv:1404.4661 (2014)

11. Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

12. Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE (2017) 4724{4733

13. Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3d residual networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE (2017) 5534{5542

14. Xu, H., Das, A., Saenko, K.: R-c3d: Region convolutional 3d network for temporal activity detection. In: The IEEE International Conference on Computer Vision (ICCV). Volume 6. (2017) 8

15. Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: Proceedings of the 2017 ACM on Multimedia Conference, ACM (2017) 988{996

27 references, page 1 of 2
Abstract
Detection of video shot transition is a crucial pre-processing step in video analysis. Previous studies are restricted on detecting sudden content changes between frames through similarity measurement and multi-scale operations are widely utilized to deal with transitions of various lengths. However, localization of gradual transitions are still under-explored due to the high visual similarity between adjacent frames. Cut shot transitions are abrupt semantic breaks while gradual shot transitions contain low-level spatial-temporal patterns caused by video effects in addition to the gradual semantic breaks, e.g. dissolve. In order to address the problem, we propos...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download fromView all 2 versions
http://arxiv.org/pdf/1808.0423...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book . 2019
Provider: Crossref
27 references, page 1 of 2

1. Yuso , Y., Christmas, W.J., Kittler, J.: Video shot cut detection using adaptive thresholding. In: BMVC. (2000) 1{10 [OpenAIRE]

2. Yuan, J., Li, J., Lin, F., Zhang, B.: A uni ed shot boundary detection framework based on graph partition model. In: Proceedings of the 13th annual ACM international conference on Multimedia, ACM (2005) 539{542

3. Lu, Z.M., Shi, Y.: Fast video shot boundary detection based on svd and pattern matching. IEEE Transactions on Image processing 22 (2013) 5136{5145

4. Hassanien, A., Elgharib, M., Selim, A., Hefeeda, M., Matusik, W.: Large-scale, fast and accurate shot boundary detection through spatio-temporal convolutional neural networks. arXiv preprint arXiv:1705.03281 (2017) [OpenAIRE]

5. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision, Springer (2016) 21{37

6. Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: A formal study of shot boundary detection. IEEE transactions on circuits and systems for video technology 17 (2007) 168{186

7. Liu, Z., Gibbon, D., Zavesky, E., Shahraray, B., Ha ner, P.: At&t research at trecvid 2007. In: Proc. TRECVID Workshop. (2007) 19{26

8. Gygli, M.: Ridiculously fast shot boundary detection with fully convolutional neural networks. arXiv preprint arXiv:1705.08214 (2017)

9. Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on, IEEE (2015) 4353{4361 [OpenAIRE]

10. Wang, J., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y., et al.: Learning ne-grained image similarity with deep ranking. arXiv preprint arXiv:1404.4661 (2014)

11. Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

12. Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE (2017) 4724{4733

13. Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3d residual networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE (2017) 5534{5542

14. Xu, H., Das, A., Saenko, K.: R-c3d: Region convolutional 3d network for temporal activity detection. In: The IEEE International Conference on Computer Vision (ICCV). Volume 6. (2017) 8

15. Lin, T., Zhao, X., Shou, Z.: Single shot temporal action detection. In: Proceedings of the 2017 ACM on Multimedia Conference, ACM (2017) 988{996

27 references, page 1 of 2
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue