Multi-camera Video Object Recognition Using Active Contours

Joanna Isabelle Olszewska

Abstract

In this paper, we propose to tackle with multiple video-object detection and recognition in a multi-camera environment using active contours. Indeed, with the growth of multi-camera systems, many computer vision frameworks have been developed, but none taking advantage of the well-established active contour method. Hence, active contours allow precise and automatic delineation of entire object's boundaries in frames, leading to an accurate segmentation and tracking of video objects displayed into the multi-view system, while our late fusion approach allows robust recognition of the detected objects in the synchronized sequences. Our active-contour-based system has been successfully tested on video-surveillance standard datasets and shows excellent performance in terms of computational efficiency and robustness compared to state-of-art ones.

References

  1. Alqaisi, T., Gledhill, D., and Olszewska, J. I. (2012). Embedded double matching of local descriptors for a fast automatic recognition of real-world objects. In Proceedings of the IEEE International Conference on Image Processing (ICIP'12), pages 2385-2388.
  2. Archetti, F., Manfredotti, C., Messina, V., and Sorrenti, D. (2006). Foreground-to-ghost discrimination in singledifference pre-processing. In Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, pages 23-30.
  3. Athanasiadis, T., Mylonas, P., Avrithis, Y., and Kollias, S. (2007). Semantic image segmentation and object labeling. IEEE Transactions on Circuits and Systems for Video Technology, 17(3):298-312.
  4. Bhat, M. and Olszewska, J. I. (2014). DALES: Automated Tool for Detection, Annotation, Labelling and Segmentation of Multiple Objects in Multi-Camera Video Streams. In Proceedings of the ACL International Conference on Computational Linguistics Workshop, pages 87-94.
  5. Black, J., Ellis, T., and Rosin, P. (2002). Multi View Image Surveillance and Tracking. In Proceedings of the IEEE Workshop on Motion and Video Computing, pages 169-174.
  6. Bryner, D. and Srivastava, A. (2014). Bayesian active contours with affine-invariant, elastic shape prior. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pages 312- 319.
  7. Chen, K.-W., Lai, C.-C., Hung, Y.-P., and Chen, C.-S. (2008). An adaptative learning method for target tracking across multiple cameras. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pages 1-8.
  8. Choi, J.-W. and Yoo, J.-H. (2013). Real-time multi-person tracking in fixed surveillance camera environment. In Proceedings of the IEEE International Conference on Consumer Electronics.
  9. Dai, X. and Payandeh, S. (2013). Geometry-based object association and consistent labeling in multi-camera surveillance. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 3(2):175-184.
  10. Diaz, R., Hallman, S., and Fowlkes, C. C. (2013). Detecting dynamic objects with multi-view background subtraction. In Proceedings of the IEEE International Conference on Computer Vision, pages 273-280.
  11. Evans, M., Osborne, C. J., and Ferryman, J. (2013). Multicamera object detection and tracking with object size estimation. In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 177-182.
  12. Farrell, R. and Davis, L. S. (2008). Decentralized discovery of camera network topology. In Proceedings of the ACM/IEEE International Conference on Distributed Smart Cameras, pages 1-10.
  13. Ferrari, V., Tuytelaars, T., and Gool, L. V. (2006). Simultaneous object recognition and segmentation from single or multiple model views. International Journal of Computer Vision, 67(2):159-188.
  14. Fleuret, F., Berclaz, J., Lengagne, R., and Fua, P. (2008). Multicamera people tracking with a probabilistic occupancy map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):267-282.
  15. Friedman, N. and Russell, S. (1997). Image segmentation in video sequences: A probabilistic approach. In Proceedings of the 13th Conference on Uncertainty in AI.
  16. Guler, S., Griffith, J. M., and Pushee, I. A. (2003). Tracking and handoff between multiple perspective camera views. In Proceedings of the 32nd IEEE Workshop on Applied Imaginary Pattern Recognition, pages 275- 281.
  17. Haralick, R. M. (1988). Mathematical morphology and computer vision. In Proceedings of the IEEE Asilomar Conference on Signals, Systems and Computers, volume 1, pages 468-479.
  18. Haritaoglu, I., Harwood, D., and Davis, L. (2000). Realtime surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 77(8):809-830.
  19. Hsu, H.-H., Yang, W.-M., and Shih, T. K. (2013). People tracking in a multi-camera environment. In Proceedings of the IEEE Conference Anthology, pages 1-4.
  20. Huang, W., Liu, Z., and Pan, W. (2007). The precise recognition of moving object in complex background. In Proceedings of 3rd IEEE International Conference on Natural Computation, volume 2, pages 246-252.
  21. Izadi, M. and Saeedi, P. (2008). Robust region-based background subtraction and shadow removing using colour and gradient information. In Proceedings of the 19th IEEE International Conference on Pattern Recognition, pages 1-5.
  22. Kettnaker, V. and Zabih, R. (1999). Bayesian multi-camera surveillance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 1-5.
  23. Kumar, K. S., Prasad, S., Saroj, P. K., and Tripathi, R. C. (2010). Multiple cameras using real-time object tracking for surveillance and security system. In Proceedings of the IEEE International Conference on Emerging Trends in Engineering and Technology, pages 213-218.
  24. Lamard, L., Chapuis, R., and Boyer, J.-P. (2013). CPHD Filter addressing occlusions with pedestrians and vehicles tracking. In Proceedings of the IEEE International Intelligent Vehicles Symposium, pages 1125- 1130.
  25. Lee, G. H., Pollefeys, M., and Fraundorfer, F. (2014). Relative pose estimation for a multi-camera system with known vertical direction. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pages 540-547.
  26. M. Kamezaki, Y. Junjie, H. I. S. S. (2014). An autonomous multi-camera control system using situation-based role assignment for tele-operated work machines. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 5971-5976.
  27. Mavrinac, A. and Chen, X. (2013). Modeling coverage in camera networks: A survey. International Journal of Computer Vision, 101(1):205-226.
  28. Olszewska, J. I. (2011). Spatio-temporal visual ontology. In Proceedings of the 1st EPSRC Workshop on Vision and Language (VL'2011).
  29. Olszewska, J. I. (2012). Multi-target parametric active contours to support ontological domain representation. In Proceedings of the RFIA Conference, pages 779-784.
  30. Olszewska, J. I. (2013). Multi-scale, multi-feature vector flow active contours for automatic multiple-face detection. In Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing.
  31. Olszewska, J. I. and McCluskey, T. L. (2011). Ontologycoupled active contours for dynamic video scene understanding. In Proceedings of the IEEE International Conference on Intelligent Engineering Systems, pages 369-374.
  32. Parker, J. R. (2010). Algorithms for Image Processing and Computer Vision. John Wiley and Sons, 2nd edition.
  33. PETS (2001). PETS Dataset. Available online at: ftp://ftp.pets.rdg.ac.uk/pub/PETS2001.
  34. Remagnino, P., Shihab, A. I., and Jones, G. A. (2004). Distributed intelligence for multi-camera visual surveillance. Pattern Recognition, 37(4):675-689.
  35. Sin, M., Su, H., Savarese, S., and Fei-Fei, L. (2009). A multi-view probabilistic model for (3D) object classes. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pages 1247-1254.
  36. Spehr, J., Rosebrock, D., Mossau, D., Auer, R., Brosig, S., and Wahl, F. M. (2011). Hierarchical scene understanding for intelligent vehicles. In Proceedings of the IEEE International Intelligent Vehicles Symposium, pages 1142-1147.
  37. Stauffer, C. and Grimson, W. (1999). Adaptive background mixture model for real-time tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  38. Toyama, K., Krumm, J., Brumitt, B., and Meyers, B. (1995). Wallflower: Principles and practice of background maintenance. In Proceedings of the IEEE International Conference on Computer Vision, volume 1, pages 255-261.
  39. Travieso, C. M., Dutta, M. K., Sole-Casals, J., and Alonso, J. B. (2014). Detection and tracking of the human hot spot. In Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, pages 325-330.
  40. Wren, C. R., Azarbayejani, A., Darrell, T., and Pentland, A. P. (1997). Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):780-785.
  41. Yao, C., Li, W., and Gao, L. (2009). An efficient moving object detection algorithm using multi-mask. In Proceedings of 6th IEEE International Conference on Fuzzy Systems and Knowledge Discovery, volume 5, pages 354-358.
  42. Zhou, H. and Kimber, D. (2006). Unusual event detection via multi-camera video mining. In Proceedings of the IEEE International Conference on Pattern Recognition, pages 1161-1166.
  43. Zivkovic, Z. and van der Heijden, F. (2004). Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):651-656.
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Paper Citation


in Harvard Style

Isabelle Olszewska J. (2015). Multi-camera Video Object Recognition Using Active Contours . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 379-384. DOI: 10.5220/0005334303790384


in Bibtex Style

@conference{mpbs15,
author={Joanna Isabelle Olszewska},
title={Multi-camera Video Object Recognition Using Active Contours},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2015)},
year={2015},
pages={379-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005334303790384},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2015)
TI - Multi-camera Video Object Recognition Using Active Contours
SN - 978-989-758-069-7
AU - Isabelle Olszewska J.
PY - 2015
SP - 379
EP - 384
DO - 10.5220/0005334303790384