Quantitative performance analysis of object detection algorithms on underwater video footage

MAED 2012 - Proceedings of the 2012 ACM Workshop on Multimedia Analysis for Ecological Data, Co-located with ACM Multimedia 2012

Volumn , Issue , 2012, Pages 57-60

  Kavasidis, Isaak ^a   Palazzo, Simone ^a  

^a UNIVERSITY OF CATANIA   (Italy)

Author keywords

Object detection; Unconstrained; Underwater

Indexed keywords

COLOR PATTERN; FISH POPULATIONS; MULTI-MODAL; OBJECT DETECTION; OBJECT DETECTION ALGORITHMS; PERFORMANCE ANALYSIS; UNCONSTRAINED; UNDERWATER; VIDEO FOOTAGE;

ECOLOGY; FISH DETECTORS; FISHERIES; MARINE BIOLOGY; OBJECT RECOGNITION;

UNDERWATER SOILS;

EID: 84870512364     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2390832.2390847     Document Type: Conference Paper

References (15)

1. O. Barnich and M. Van Droogenbroeck. ViBe: A universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing, 20(6):1709-1724, June 2011.
2. A. Faro, D. Giordano, and C. Spampinato. Adaptive background modeling integrated with luminosity sensors and occlusion processing for reliable vehicle detection. Appearing on IEEE Transactions on Intelligent Transportation Systems, 2011.
3. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting, 1995.
4. I. Kavasidis, S. Palazzo, R. Di Salvo, D. Giordano, and C. Spampinato. A semi-automatic tool for detection and tracking ground truth generation in videos. In VIGTA '12: Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications, pages 1-5, New York, NY, USA, 2012. ACM.
5. K. Kim, T. Chalidabhongse, D. Harwood, and L. Davis. Background modeling and subtraction by codebook construction. In Image Processing, 2004. ICIP '04. 2004 International Conference on, volume 5, pages 3061 - 3064 Vol. 5, oct. 2004. (Pubitemid 40820720)
6. S. G. Narasimhan and S. K. Nayar. Vision and the atmosphere. Int. J. Comput. Vision, 48(3):233-254, July 2002. (Pubitemid 34959710)
7. S. K. Nayar and S. G. Narasimhan. Vision in bad weather. In Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2, ICCV '99, pages 820-, Washington, DC, USA, 1999. IEEE Computer Society.
8. F. Porikli. Multiplicative background-foreground estimation under uncontrolled illumination using intrinsic images. In in Proc. of IEEE Motion Multi-Workshop, 2005.
9. F. Porikli. Achieving real-time object detection and tracking under extreme conditions. Journal of Real-Time Image Processing, 1:33-40, 2006. 10.1007/s11554-006-0011-z. (Pubitemid 44411828)
10. F. Porikli and C. Wren. Change detection by frequency decomposition: Wave-back. In Proc. of Workshop on Image Analysis for Multimedia Interactive Services, 2005.
11. Y. Sheikh and M. Shah. Bayesian object detection in dynamic scenes. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 74 - 79 vol. 1, june 2005. (Pubitemid 41275925)
12. C. Spampinato, Y.-H. Chen-Burger, G. Nadarajan, and R. Fisher. Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos, volume 2, pages 514-519. 2008.
13. C. Spampinato, D. Giordano, R. Di Salvo, Y.-H. J. Chen-Burger, R. B. Fisher, and G. Nadarajan. Automatic fish classification for underwater species behavior understanding. In Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams, ARTEMIS '10, pages 45-50, New York, NY, USA, 2010. ACM.
14. C. Spampinato, S. Palazzo, B. Boom, J. van Ossenbruggen, I. Kavasidis, R. Di Salvo, F.-P. Lin, D. Giordano, L. Hardman, and R. Fisher. Understanding fish behavior during typhoon events in real-life underwater environments. Multimedia Tools and Applications, pages 1-38. 10.1007/s11042-012-1101-5.
15. C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. Proceedings 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Cat No PR00149, 2(c):246-252, 1999.