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Unsupervised detection of ruptures in spatial relationships in video sequences based on log-likelihood ratio

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Abstract

In this work, we propose a new approach to automatically detect ruptures in spatial relationships in video sequences, based on low-level primitives, in unsupervised manner. The spatial relationships between two objects of interest are modeled using angle and distance histograms as examples. The evolution of the spatial relationships during time is estimated from the distances between two successive angle or distance histograms and then considered as a temporal signal. The evolution of a spatial relationship is modeled by a linear Gaussian model. Then, two hypotheses “without change” and “with change” are considered, and a log-likelihood ratio is computed. The distribution of the log-likelihood ratio, given that \(H_0\) is true, is estimated and used to compute the p value. The comparison of this p value to a significance level \(\alpha \), expressing the probability of false alarms, allows us to detect significant ruptures in spatial relationships during time. In addition, this approach is generalized to detect multiple object events such as merging, splitting, and other events that contain ruptures in their spatial relationships evolution. This work shows that the description of spatial relationships across time is a promising step toward event detection.

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Notes

  1. See http://www.cs.ubc.ca/~lowe/vision.html for examples of companies and projects on these topics.

References

  1. Visam Project (1997) http://www.cs.cmu.edu/~vsam/

  2. Icons Project (2000) http://www.dcs.qmul.ac.uk/research/vision/projects/ICONS/

  3. Advisor Project (2000) http://www-sop.inria.fr/orion/ADVISOR/

  4. Etiseo Project (2004) http://www-sop.inria.fr/orion/ETISEO/

  5. Caretaker Project (2006) http://www-sop.inria.fr/members/Francois.Bremond/topicsText/caretakerProject.html

  6. Avitrackr Project (2004) http://www-sop.inria.fr/members/Francois.Bremond/topicsText/avitrackProject.html

  7. Beware Project (2007) http://www.eecs.qmul.ac.uk/~sgg/BEWARE/

  8. Piciarelli C, Micheloni C, Foresti G (2008) Trajectory-based anomalous event detection. IEEE Trans Circ Syst Video Technol 18:1544–1554

    Article  Google Scholar 

  9. Saleemi I, Shafique K, Shah M (2009) Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Trans Pattern Anal Mach Intell 31(8):1472–1485

    Article  Google Scholar 

  10. Hu W, Xiao X, Fu Z, Xie D, Tan T, Maybank S (2006) A system for learning statistical motion patterns. IEEE Trans Pattern Anal Mach Intell 28:1450–1464

    Article  Google Scholar 

  11. Wang T, Snoussi H (2014) Detection of abnormal visual events via global optical flow orientation histogram. IEEE Trans Inf Forensics Secur 9(6):988–998

    Article  Google Scholar 

  12. Li A, Miao Z, Cen Y, Wang T, Voronin V (2015) Histogram of maximal optical flow projection for abnormal events detection in crowded scenes. Int J Distrib Sens Netw 11:1–11

    Google Scholar 

  13. Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30:555–560

    Article  Google Scholar 

  14. Kratz L, Nishino K (2009) Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE conference on computer vision and pattern recognition, pp 1446–1453

  15. Jiang F, Wu Y, Katsaggelos AK (2009) Detecting contextual anomalies of crowd motion in surveillance video. In: 16th IEEE international conference on image processing, pp 1117–1120

  16. Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition, pp 935–942

  17. Cong Y, Yuan J, Tang Y (2013) Video anomaly search in crowded scenes via spatio-temporal motion context. IEEE Trans Inf Forensics Secur 8:1590–1599

    Article  Google Scholar 

  18. Cong Y, Yuan J, Liu J (2013) Abnormal event detection in crowded scenes using sparse representation. Pattern Recognit 46:1851–1864

    Article  Google Scholar 

  19. Hu X, Hu S, Zhang X, Zhang H, Luo L (2014) Anomaly detection based on local nearest neighbor distance descriptor in crowded scenes. Sci World J 2014:1–12

    Google Scholar 

  20. Tran D, Yuan J, Forsyth D (2014) Video event detection: from subvolume localization to spatio-temporal path search. IEEE Trans Pattern Anal Mach Intell 36(12):404–416

    Article  Google Scholar 

  21. Dan DX, Ricci E, Yan Y, Song J, Sebe N (2015) Learning deep representations of appearance and motion for anomalous event detection. British Machine Vision Conference

  22. Ren H, Liu W, Olsen SI, Escalera S, Moeslund TB (2015) Unsupervised behavior-specific dictionary learning for abnormal event detection. British Machine Vision Conference, pp 1–28

  23. Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: IEEE international conference on computer vision, pp 2720–2727

  24. Zhao B, Fei-Fei L, Xing E (2001) Online detection of unusual events in videos via dynamic sparse coding. In: IEEE conference on computer vision and pattern recognition, pp 3313–3320

  25. Cheng K, Chen Y, Fang W (2015) Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. In: IEEE conference on computer vision and pattern recognition, pp 2909–2917

  26. Basharat A, Gritai A, Shah M (2008) Learning object motion patterns for anomaly detection and improved object detection. In: IEEE conference on computer vision and pattern recognition, pp 1–8

  27. Solmaz B, Moore B, Shah M (2012) Identifying behaviors in crowd scenes using stability analysis for dynamical systems. IEEE Trans Pattern Anal Mach Intell 34:2064–2070

    Article  Google Scholar 

  28. Saleemi I, Hartung L, Shah M (2010) Scene understanding by statistical modeling of motion patterns. In: IEEE conference on computer vision and pattern recognition, pp 2069–2076

  29. Tzelepis C, Mezaris V, Patras I (2016) Video event detection using kernel support vector machine with isotropic gaussian sample uncertainty KSVM-iGSU. In: International conference on multimedia modeling, pp 3–15

  30. Mazloom M, Li X, Snoek CG (2016) TagBook: a semantic video representation without supervision for event detection. IEEE Trans Multimed 18:1378–1388

    Article  Google Scholar 

  31. Li Z, Liu J, Tang J, Lu H (2015) Robust structured subspace learning for data representation. IEEE Trans Pattern Anal Mach Intell 37:2085–2098

    Article  Google Scholar 

  32. Li Z, Tang J (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimed 17:1989–1999

    Article  Google Scholar 

  33. Abou-Elailah A, Gouet-Brunet V, Bloch I (2015) Detection of ruptures in spatial relationships in video sequences. In: International conference on pattern recognition applications and methods, pp 110–120

  34. Tissainayagam P, Suter D (2005) Object tracking in image sequences using point features. Pattern Recognit 38:105–113

    Article  Google Scholar 

  35. Zhou H, Yuan Y, Shi C (2009) Object tracking using SIFT features and mean shift. Comput Vis Image Underst 113(3):345–352

    Article  Google Scholar 

  36. Miyajima K, Ralescu A (1994) Spatial organization in 2D images. In: Third IEEE conference on fuzzy systems, pp 100–105

  37. Hafner J, Sawhney H, Equitz W, Flickner M, Niblack W (1995) Efficient color histogram indexing for quadratic form distance functions. IEEE Trans Pattern Anal Mach Intell 17:729–736

    Article  Google Scholar 

  38. Bloch I, Atif J (2015) Hausdorff distances between distributions using optimal transport and mathematical morphology. In: Mathematical morphology and its applications to signal and image processing, pp 522–534

  39. Bloch I, Atif J (2016) Defining and computing Hausdorff distances between distributions on the real line and on the circle: link between optimal transport and morphological dilations. Math Morphol Theory Appl 1:79–99

    Google Scholar 

  40. Zhang L, van der Maaten L (2013) Structure preserving object tracking. In: IEEE conference on computer vision and pattern recognition, pp 1838–1845

  41. Widynski N, Dubuisson S, Bloch I (2012) Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking. Comput Vis Image Underst 116:1076–1094

    Article  Google Scholar 

  42. Morimitsu H, Roberto M, Bloch I (2014) A spatio-temporal approach for multiple object detection in videos using graphs and probability maps. In: International conference on image analysis and recognition, pp 421–428

  43. Morimitsu H, Bloch I, Cesar RM (2017) Exploring structure for long-term tracking of multiple objects in sports videos. Comput Vis Image Underst 159:89–104

    Article  Google Scholar 

  44. Harris C, Stephens M (1988) A combined corner and edge detector. In: Fourth Alvey vision conference, pp 147–151

  45. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  46. Basseville M, Nikiforov IV (1993) Detection of abrupt changes: theory and application. Prentice Hall, Englewood Cliffs, p 104

    Google Scholar 

  47. PETS (2006) http://www.cvg.rdg.ac.uk/PETS2006/data.html

  48. PETS (2009) http://www.cvg.rdg.ac.uk/PETS2009/a.html

  49. Bazzani L, Cristani M, Murino V (2012) Decentralized particle filter for joint individual-group tracking. In: IEEE conference on computer vision and pattern recognition, pp 1886–1893

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Authors and Affiliations

  1. LTCI, Télécom ParisTech, Université Paris-Saclay, 75013, Paris, France

    Abdalbassir Abou-Elailah & Isabelle Bloch

  2. LaSTIG MATIS, IGN, ENSG, Univ. Paris-Est, 94160, Saint-Mande, France

    Valerie Gouet-Brunet

Authors
  1. Abdalbassir Abou-Elailah
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  2. Isabelle Bloch
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  3. Valerie Gouet-Brunet
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Correspondence to Abdalbassir Abou-Elailah.

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Abou-Elailah, A., Bloch, I. & Gouet-Brunet, V. Unsupervised detection of ruptures in spatial relationships in video sequences based on log-likelihood ratio. Pattern Anal Applic 21, 829–846 (2018). https://doi.org/10.1007/s10044-017-0669-9

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