Static and moving object detection using flux tensor with split gaussian models

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn , Issue , 2014, Pages 420-424

  Wang, Rui ^a   Bunyak, Filiz ^a   Seetharaman, Guna ^b   Palaniappan, Kannappan ^a  

^a University of Missouri   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

background subtraction; change detection; flux tensor; Gaussian model; motion detection

Indexed keywords

ARTS COMPUTING; COMPUTER VISION; GAUSSIAN DISTRIBUTION; HYBRID SYSTEMS; OBJECT RECOGNITION; STATISTICAL TESTS; TENSORS;

BACKGROUND SUBTRACTION; BENCHMARK DATASETS; CHANGE DETECTION; COMPUTATION METHODS; GAUSSIAN MODEL; ILLUMINATION CHANGES; MOTION DETECTION; MOVING-OBJECT DETECTION;

OBJECT DETECTION;

EID: 84908508432     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2014.68     Document Type: Conference Paper

References (14)

1. O. Barnich and M. Van Droogenbroeck. Vibe: A universal background subtraction algorithm for video sequences. IEEE Trans. Image Proc., 20(6):1709-1724, 2011.
2. Y. Benezeth, P.-M. Jodoin, B. Emile, C. Rosenberger, and H. Laurent. Comparative study of background subtraction algorithms. Journal of Electronic Imaging, 19(3):033003-033003-12, 2010.
3. F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman. Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking. Journal of Multimedia, 2(4):20, 2007.
4. F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman. Geodesic active contour based fusion of visible and infrared video for persistent object tracking. In IEEEWorkshop Applications of Computer Vision (WACV), 2007.
5. A. Elgammal, D. Harwood, and L. Davis. Nonparametric model for background subtraction. In European Conf. Comp. Vision. Springer, 2000.
6. R. H. Evangelio and T. Sikora. Complementary background models for the detection of static and moving objects in crowded environments. In IEEE Int. Conf. Advanced Video Signal-Based Surveillance, 2011.
7. N. Goyette, P. Jodoin, F. Porikli, J. Konrad, and P. Ishwar. Changedetection. net: A new change detection benchmark dataset. In IEEE Conf. Comp. Vision Pattern Recog. Workshops, pages 1-8, 2012.
8. K. Palaniappan, F. Bunyak, S. Nath, and J. Goffeney. Parallel processing strategies for cell motility and shape analysis. In High-Throughput Image Reconstruction and Analysis, pages 39-87. Artech House Publishers, 2009.
9. K. Palaniappan, I. Ersoy, and S. K. Nath. Moving object segmentation using the flux tensor for biological video microscopy. Lecture Notes in Computer Science (PCM), 4810:483-493, 2007.
10. K. Palaniappan, I. Ersoy, G. Seetharaman, S. Davis, P. Kumar, R. M. Rao, and R. Linderman. Parallel flux tensor analysis for efficient moving object detection. In 14th Int. Conf. Information Fusion, 2011.
11. R. Radke, S. Andra, O. Al-Kofahi, and B. Roysam. Image change detection algorithms: a systematic survey. IEEE Trans. Image Proc., 14(3):294-307, 2005.
12. C. Stauffer and W. E. L. Grimson. Adaptive background mixture models for real-time tracking. In IEEE Conf. Comp. Vision Pattern Recog., volume 2, 1999.
13. Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In Int. Conf. Pattern Recog., volume 2, 2004.
14. Z. Zivkovic and F. van der Heijden. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett., 27(7):773-780, May 2006.