Background Subtraction

1. Performance evaluation with ground truth (6 papers)

ROC Analysis

J. Davis, M. Goadrich, "The Relationship between Precision-Recall and ROC Curves", International Conference on Machine Learning, ICML 2006, Volume 148, pages 233-240, Pittsburgh, Pennsylvania, 2006.

Similarity Measures (S)

L. Li, W. Huang, I. Gu, Q.Tian, “Statistical Modeling of Complex Background for Foreground Object Detection”, IEEE Transaction on Image Processing, Volume 13, Issue 11, pages 1459-1472, November 2004.

F-measure

N. Lazarevic-McManus, J. Renno, D. Makris , G. Jones , “Performance Evaluation in Visual Surveillance using the F-Measure”, VSSN 2006, USA, October 2006.

N. Lazarevic-McManus, J. Renno, D. Makris, G. Jones, “An Object-based Comparative Methodology for Motion Detection based on the F-Measure”, Computer Vision and Image Understanding, 2007.

Spatial Accuracy Metric

L. Liu, N. Sang, “Metrics for Objective Evaluation of Background Subtraction Algorithms”, International Conference on Image and Graphics, ICIG 2011, Hefei, Anhui, China, August 2011.

Temporal Stability Metric

L. Liu, N. Sang, “Metrics for Objective Evaluation of Background Subtraction Algorithms”, International Conference on Image and Graphics, ICIG 2011, Hefei, Anhui, China, August 2011.

Rate of Background Detection (RBD)

M. Hassan, A. Malik, N. Saad, D. Fofi, “Evaluation Metric for Rate of Background Detection”, IEEE International Instrumentation and Measurement Technology Conference, Taipei, Taiwan, May 2016.

Documentation/Auto-adaptability/Performance/Speed/Novelty

M. Chacon-Murguia, A. Guzman-Pando, G. Ramirez-Alonso, J. Ramirez-Quintana, "A novel instrument to compare dynamic object detection algorithms", Image and Vision Computing, pages 19-28, 2019. [Website]

2. Performance evaluation without ground truth (11 papers)

J. San Miguel, J. Martinez, “On the evaluation of background subtraction algorithms without ground-truth”, International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010, Boston, USA, September 2010.

L. Sun, W. Sheng, Y. Liu, “Background modeling and its evaluation for complex scenes”, Multimedia Tools Applications, 2014.

Perturbation Detection Rate Analysis (PDR)

T. Chalidabhongse, K. Kim, D. Harwood, L. Davis, “A Perturbation Method for Evaluating Background Subtraction Algorithms”, Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS 2003, October 2003.

K. Kim, T. H. Chalidabhongse, D. Harwood, L. Davis, “PDR: Performance Evaluation Method for Foreground-Background Segmentation Algorithms”, EURASIP Journal on Applied Signal Processing, 2006.

Boundary based evaluation

P. Correia, F. Pereira, “Stand-Alone Objective Segmentation Quality Evaluation”, EURASIP Journal on Applied Signal Processing, ASP 2002, pages 389-400, 2002

C. Erdem, B. Sankur, A. Tekalp, “Performance Measures for Video Object Segmentation and Tracking”, IEEE Image Processing, Volume 13, Issue 7, pages 937-950, July 2004.

Model based evaluation

M. Harville, “A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian background models”, European Conference on Computer Vision, ECCV 2002, Copenhagen, Denmark, May 2002.

S. Cheung, C. Kamath, “Robust Background Subtraction with Foreground Validation for Urban Traffic Video”, EURASIP Journal of Applied Signal Processing, Special Issue on Advances in Intelligent Vision Systems: Methods and Applications, New York, USA, 2005.

M. Rincon, E. Carmona, M. Bachiller, E. Folgado, “Segmentation of Moving Objects with Information Feedback Between Description Levels”, IWINAC 2007, pages 171-181, 2007.

Assisted based evaluation

C. O’Conaire, N. O’Connor, A. Smeaton, “Detector adaptation by maximizing agreement between independent data sources”, OTCBVS 2007, pages 1-6, 2007.

A. García, J. Bescos, “Video Object Segmentation Based on Feedback Schemes Guided by a Low-Level Scene Ontology”, ACVIS 2008, pages 322-333, 2009.

3. Methodology (3 papers)

S. Pierard, M. Van Droogenbroeck, “Summarizing the performances of a background subtraction algorithm measured on several videos”, Preprint, 2020.

S. Sanches, A. Sementille, I. Aguilar, V. Freire, “Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems”, Multimedia Tools Applications, 2020.

C. Silva, K. Rosa, P. Bugatti, P. Saito, C. Correa, R. Yokoyama, and S. Sanches. Method for selecting representative videos for change detection datasets. Multimedia Tools and Applications, November 2021.