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Localization of region of interest in surveillance scene

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Abstract

In this paper, we present a method for autonomously detecting and extracting region(s)-of-interest (ROI) from surveillance videos using trajectory-based analysis. Our approach, localizes ROI in a stochastic manner using correlated probability density functions that model motion dynamics of multiple moving targets. The motion dynamics model is built by analyzing trajectories of multiple moving targets and associating importance to regions in the scene. The importance of each region is estimated as a function of the total time spent by multiple targets, their instantaneous velocity and direction of movement whilst passing through that region. We systematically validate our model and benchmark our technique against competing baselines through extensive experimentation using public datasets such as CAVIAR, ViSOR, and CUHK as well as a scenario-specific in-house surveillance dataset. Results obtained have demonstrated the superiority of the proposed technique against a few popular existing state-of-the-art techniques.

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Notes

  1. http://www.openvisor.org

  2. http://www.mathworks.in/help/vision/examples/abandoned-object-detection.html.

References

  1. Bharath R, Nicholas L, Cheng X (2013) Scalable scene understanding using saliency-guided object localization. In: 10th IEEE International Conference on Control and Automation, pp 1503–1508

  2. Bao X, Javanbakhti S, Zinger S, Wijnhoven R, de With P (2014) Context-based object-of-interest detection for a generic traffic surveillance analysis system. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp 1087–1090

  3. Bharath R, Nicholas L, Cheng X (2013) Scalable scene understanding using saliency-guided object localization. In: Proceedings of the IEEE International Conference on Control and Automation, pp 1503–1508

  4. Brun L, Saggese A, Vento M (2014) Dynamic scene understanding for behavior analysis based on string kernels. IEEE Trans Circuits Syst Video Technol 24 (10):1669–1681

    Article  Google Scholar 

  5. Colque R, Jnior C, Schwartz W (2015) Histograms of optical flow orientation and magnitude to detect anomalous events in videos. In: SIBGRAPI conference on Graphics, Patterns and Images, pp 126–133

  6. Dinh T, Vo N, Medioni G (2011) Context tracker Exploring supporters and distracters in unconstrained environments. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1177–1184

  7. Dogra D, Ahmed A, Bhaskar H (2015) Interest area localization using trajectory analysis in surveillance scenes. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, pp 478–485

  8. Dogra D, Reddy R, Subramanyam K, Ahmed A, Bhaskar H (2015) Scene representation and anomalous activity detection using weighted region association graph. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, pp 31–38

  9. Dogra D, Ahmed A, Bhaskar H (2015) Smart video summarization using mealy machine-based trajectory modelling for surveillance applications. Multimedia Tools and Applications, 1–29. doi:10.1007/s11042-015-2576-7

  10. Fisher R, Santos-Victor J, Crowley J (2001) Caviar: Context aware vision using image-based active recognition. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/. Accessed: July 2014

  11. Mathworks Inc (2014) Abandoned object detection http://www.mathworks.in/help/vision/examples/abandoned-object-detection.html. Accessed: July 2014

  12. Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: a discriminative regional feature integration approach. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2083–2090

  13. Javanbakhti S, Zinger S, de With P (2014) Context-based region labeling for event detection in surveillance video. In: International Conference on Information Science, Electronics and Electrical Engineering, vol 1, pp 94–98

  14. Kapsalas P, Rapantzikos K, Sofou A, Avrithis Y (2008) Regions of interest for accurate object detection. In: Proceedings of the International Workshop on Content-Based Multimedia Indexing, pp 147–154

  15. Keum J, Lee H, Hagiwara M (2012) Mean shift-based sift keypoint filtering for region-of-interest determination. In: Proceedings of the International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems, pp 266–271

  16. Kim G, Torralba A (2009) Unsupervised detection of regions of interest using iterative link analysis, pp 961–969

  17. Lai Y, Yang C (2015) Video object retrieval by trajectory and appearance. IEEE Trans Circuits Syst Video Technol 25(6):1026–1037

    Article  Google Scholar 

  18. Lee W, Huang T, Yeh S, Chen H (2011) Learning-based prediction of visual attention for video signals. IEEE Trans Image Process 20(11):3028–3038

    Article  MathSciNet  Google Scholar 

  19. Li J, Tian Y, Huang T, Gao W (2010) Probabilistic multi-task learning for visual saliency estimation in video. Int J Comput Vis 90(2):150–165

    Article  Google Scholar 

  20. Lin W, Zhang Y, Lu J, Zhou B, Wang J, Zhou Y (2015) Summarizing surveillance videos with local-patch-learning-based abnorMality detection, blob sequence optimization, and type-based synopsis. Neurocomputing 155(0):84–98

    Article  Google Scholar 

  21. Liu T, Zheng N, Ding W, Yuan Z (2008) Video attention: Learning to detect a salient object sequence. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp 1–4

  22. Loy C, Xiang T, Gong S (2009) Multi-camera activity correlation analysis. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1988–1995

  23. Manikandan M, Soman K (2012) A novel method for detecting r-peaks in electrocardiogram (ecg) signal. Biomed Signal Process Control 7(2):118–128

    Article  Google Scholar 

  24. Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct?. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1139–1146

  25. Mitri S, Frintrop S, Pervolz K, Surmann H, Nuchter A (2005) Robust object detection at regions of interest with an application in ball recognition. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp 125–130

  26. Morris B, Trivedi M (2008) Learning and classification of trajectories in dynamic scenes: a general framework for live video analysis. In: Proceedings of the IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp 154–161

  27. Osberger W, Rohaly A (2001) Automatic detection of regions of interest in complex video sequences. In: Proceedings of the Photonics West-Electronic Imaging, pp 361–372

  28. Piciarelli C, Micheloni C, Foresti G (2008) Trajectory-based anomalous event detection. IEEE Trans Circuits Syst Video Technol 18(11):1544–1554

    Article  Google Scholar 

  29. Rahtu E, Kannala J, Salo M, Heikkilä J. (2010) Segmenting salient objects from images and videos. In: Proceedings of the European Conference on Computer Vision. Springer, pp 366–379

  30. Rokunuzzaman M, Sekiyama K, Fukuda T (2010) Automatic roi detection and evaluation in video sequences based on human interest. J Rob Mechatronics 22 (1):65–75

    Article  Google Scholar 

  31. 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 

  32. Shou N, Peng H, Wang H, Meng L, Du K (2012) An rois based pedestrian detection system for single images. In: Proceedings of the International Congress on Image and Signal Processing, pp 1205– 1208

  33. Suzuki N, Hirasawa K, Tanaka K, Kobayashi Y, Sato Y, Fujino Y (2007) Learning motion patterns and anomaly detection by human trajectory analysis. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp 498–503

  34. Uddin M, Ravishankar C, Tsotras V (2011) Finding regions of interest from trajectory data. In: IEEE International Conference on Mobile Data Management, vol 1, pp 39–48

  35. Vezzani R, Cucchiara R (2010) Video surveillance online repository (visor): an integrated framework. Multimedia Tools Appl 50(2):359–380

    Article  Google Scholar 

  36. Wang W, Lin W, Chen Y, Wu J, Wang J, Sheng B (2014) Finding coherent motions and semantic regions in crowd scenes: a diffusion and clustering approach. In: Proceedings of the European Conference on Computer Vision, volume 8689 of Lecture Notes in Computer Science, pp 756– 771

  37. Wu T, Vu C, Cheng Q, Chandler D (2009) Region-of-importance detection based on fusion of audio and video. In: Forty-Third Asilomar Conference on Signals, Systems and Computers, pp 1673– 1677

  38. Wang J, Wang Y, Zhang Z (2011) Interesting region detection in aerial video using Bayesian topic models. In: First Asian Conference on Pattern Recognition, pp 706–710

  39. Wang X, Tieu K, Grimson E (2006) Learning semantic scene models by trajectory analysis. In: Proceedings of the European Conference on Computer Vision. Springer, pp 110–123

  40. Xiang M, Bashir F, Khokhar A, Schonfeld D (2009) Event analysis based on multiple interactive motion trajectories. IEEE Trans Circuits Syst Video Technol 19 (3):397–406

    Article  Google Scholar 

  41. Xu D, Wu X, Song D, Li N, Chen Y (2013) Hierarchical activity discovery within spatio-temporal context for video anomaly detection. In: Proceedings of the IEEE International Conference on Image Processing, pp 3597–3601

  42. Xuan M, Monga V, Bala R, Zhigang F (2014) Adaptive sparse representations for video anomaly detection. IEEE Trans Circuits Syst Video Technol 24(4):631–645

    Article  Google Scholar 

  43. Yan Y, Ricci E, Subramanian R, Liu G, Lanz O, Sebe N (2015) A Multi-task Learning Framework for Head Pose Estimation under Target Motion. IEEE Trans Pattern Anal Mach Intell PP(99):1– 1

    Google Scholar 

  44. Yan Y, Ricci E, Liu G, Sebe N (2015) Egocentric daily activity recognition via multitask clustering. IEEE Trans Image Process 24(10):2984–2995

    Article  MathSciNet  Google Scholar 

  45. Zhai Y, Shah M (2006) Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th annual ACM international conference on Multimedia, pp 815– 824

  46. Zhou B, Wang X, Tang X (2012) Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2871–2878

  47. Zhou Y, Yan S, Huang T (2007) Detecting anomaly in videos from trajectory similarity analysis. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp 1087– 1090

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Authors and Affiliations

  1. Haldia Institute of Technology, Haldia, India

    Sk. Arif Ahmed

  2. Indian Institute of Technology, Bhubaneswar, India

    Debi Prosad Dogra

  3. National Institute of Technology, Durgapur, India

    Samarjit Kar

  4. Sookmyung Women’s University, Seoul, Republic of Korea

    Byung-Gyu Kim

  5. University of Bristol, Bristol, UK

    Paul Hill

  6. Khalifa University, P.O.Box 127788, Abu Dhabi, United Arab Emirates

    Harish Bhaskar

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  1. Sk. Arif Ahmed
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  2. Debi Prosad Dogra
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  3. Samarjit Kar
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  4. Byung-Gyu Kim
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  5. Paul Hill
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  6. Harish Bhaskar
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Correspondence to Debi Prosad Dogra.

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Ahmed, S.A., Dogra, D.P., Kar, S. et al. Localization of region of interest in surveillance scene. Multimed Tools Appl 76, 13651–13680 (2017). https://doi.org/10.1007/s11042-016-3762-y

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