Learning a visual attention model for adaptive fast-forward in video surveillance

ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

Volumn 2, Issue , 2012, Pages 25-32

  Ḧoferlin, Benjamin ^a   Pfl̈uger, Hermann ^a   Ḧoferlin, Markus ^a   Heidemann, Gunther ^b   Weiskopf, Daniel ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

^b UNIVERSITY OF OSNABRÜCK   (Germany)

Author keywords

Adaptive fast forward; Video surveillance; Visual attention

Indexed keywords

FAST FORWARD; FIELD OF VIEWS; PERSON DETECTOR; RECTANGLE FEATURES; RELEVANCE FEEDBACK; SEMANTIC MODEL; TOPDOWN; VIDEO SURVEILLANCE; VISUAL ATTENTION; VISUAL ATTENTION MODEL;

PATTERN RECOGNITION; SEMANTICS;

SECURITY SYSTEMS;

EID: 84862234015     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Cheng, K., Luo, S., Chen, B., and Chu, H. (2009). Smartplayer: user-centric video fast-forwarding. In Proceedings of the International Conference on Human Factors in Computing Systems (CHI), pages 789-798. ACM New York.
2. Davis, J., Morison, A., and Woods, D. (2007). An adaptive focus-of-attention model for video surveillance and monitoring. Machine Vision and Applications, 18:41- 64.
3. Höferlin, B., Höferlin, M., Weiskopf, D., and Heidemann, G. (2011). Information-based adaptive fast-forward for visual surveillance. Multimedia Tools and Applications, 55(1):127-150.
4. Itti, L. (2005). Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes. Visual Cognition, 12(6):1093-1123.
5. Itti, L. and Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3):194-203.
6. Itti, L., Koch, C., and Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259.
7. Jasso, H. and Triesch, J. (2007). Learning to attend - from bottom-up to top-down. In Paletta, L. and Rome, E., editors, Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint, volume 4840 of Lecture Notes in Computer Science, pages 106-122. Springer Berlin/Heidelberg.
8. Judd, T., Ehinger, K., Durand, F., and Torralba, A. (2009). Learning to predict where humans look. In International Conference on Computer Vision, pages 2106- 2113. IEEE.
9. Kienzle, W., Schölkopf, B., Wichmann, F., and Franz, M. (2007). How to find interesting locations in video: a spatiotemporal interest point detector learned from human eye movements. In Proceedings of the DAGM Conference on Pattern Recognition, pages 405-414. Springer.
10. Nataraju, S., Balasubramanian, V., and Panchanathan, S. (2009). Learning attention based saliency in videos from human eye movements. In Workshop on Motion and Video Computing (WMVC), pages 1-6. IEEE.
11. Peker, K. and Divakaran, A. (2004). Adaptive fast playback-based video skimming using a compresseddomain visual complexity measure. In International Conference on Multimedia and Expo, volume 3, pages 2055-2058.
12. Peters, R. and Itti, L. (2007). Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention. In In Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 1-8. IEEE.
13. Petrovic, N., Jojic, N., and Huang, T. (2005). Adaptive video fast forward. Multimedia Tools and Applications, 26(3):327-344.
14. Viola, P. and Jones, M. J. (2001). Robust real-time object detection. Technical Report CRL 2001/01, Cambridge Research Laboratory.
15. Wolfe, J. (1994). Guided search 2.0 a revised model of visual search. Psychonomic Bulletin & Review, 1(2):202-238.
16. Zhao, Q. and Koch, C. (2011). Learning visual saliency. In In Proceedings of the Annual Conference on Information Sciences and Systems (CISS), pages 1-6.