HDR Imaging for Enchancing People Detection and Tracking in Indoor Environments

Panagiotis Agrafiotis, Elisavet K. Stathopoulou, Andreas Georgopoulos, Anastasios Doulamis

2015

Abstract

Videos and image sequences of indoor environments with challenging illumination conditions often capture either brightly lit or dark scenes where every single exposure may contain overexposed and/or underexposed regions. High Dynamic Range (HDR) images contain information that standard dynamic range ones, often mentioned also as low dynamic range images (SDR/LDR) cannot capture. This paper investigates the contribution of HDR imaging in people detection and tracking systems. In order to evaluate this contribution of the HDR imaging in the accuracy and robustness of pedestrian detection and tracking in challenging indoor visual conditions, two state of the art trackers of different complexity were implemented. To this direction data were collected taking into account the requirements and real-life indoor scenarios and HDR frames were produced. The algorithms were applied to the SDR data and their corresponding HDR data and were compared and evaluated for their robustness and accuracy in terms of precision and recall. Results show that that the use of HDR images enhances the performance of the detection and tracking scheme, making it robust and more reliable.

References

  1. Agrafiotis, P., Doulamis, A., Doulamis, N., and Georgopoulos, A. (2014a). Multi-sensor target detection and tracking system for sea ground borders surveillance. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, page 41. ACM.
  2. Agrafiotis, P., Georgopoulos, A., Doulamis, A. D., and Doulamis, N. D. (2014b). Precise 3d measurements for tracked objects from synchronized stereo-video sequences. In Advances in Visual Computing, pages 757-769. Springer.
  3. Alahi, A., Vandergheynst, P., Bierlaire, M., and Kunt, M. (2010). Cascade of descriptors to detect and track objects across any network of cameras. Computer Vision and Image Understanding, 114(6):624-640.
  4. Avidan, S. (2004). Support vector tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(8):1064-1072.
  5. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346-359.
  6. Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: Binary robust independent elementary features. In Computer Vision-ECCV 2010, pages 778- 792. Springer.
  7. Cehovin, L., Kristan, M., and Leonardis, A. (2011). An adaptive coupled-layer visual model for robust visual tracking. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 1363-1370. IEEE.
  8. Chermak, L. and Aouf, N. (2012). Enhanced feature detection and matching under extreme illumination conditions with a hdr imaging sensor. In Cybernetic Intelligent Systems (CIS), 2012 IEEE 11th International Conference on, pages 64-69. IEEE.
  9. Chermak, L., Aouf, N., and Richardson, M. (2014). HDR imaging for feature tracking in challenging visibility scenes. Kybernetes, 43(8):1129-1149.
  10. Collins, R. T., Liu, Y., and Leordeanu, M. (2005). Online selection of discriminative tracking features. PatDalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893. IEEE.
  11. Debevec, P. E. and Malik, J. (1997). Recovering high dynamic range radiance maps from photographs. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 7897, pages 369-378, New York, NY, USA. ACM Press/Addison-Wesley Publishing Co.
  12. Doulamis, A., Doulamis, N., Ntalianis, K., and Kollias, S. (2003). An efficient fully unsupervised video object segmentation scheme using an adaptive neuralnetwork classifier architecture. Neural Networks, IEEE Transactions on, 14(3):616-630.
  13. Doulamis, N. (2010). Iterative motion estimation constrained by time and shape for detecting persons' falls. In Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, page 62. ACM.
  14. Drago, F., Myszkowski, K., Annen, T., and Chiba, N. (2003). Adaptive logarithmic mapping for displaying high contrast scenes. Computer Graphics Forum, 22:419-426.
  15. Durand, F. and Dorsey, J. (2002). Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph., 21(3):257-266.
  16. Fattal, R., Agrawala, M., and Rusinkiewicz, S. (2007). Multiscale shape and detail enhancement from multi-light image collections. ACM Transactions on Graphics (Proc. SIGGRAPH), 26(3).
  17. Grabner, H., Leistner, C., and Bischof, H. (2008). Semi-supervised on-line boosting for robust tracking. In Computer Vision-ECCV 2008, pages 234-247. Springer.
  18. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Alvey vision conference, volume 15, page 50. Manchester, UK.
  19. Henriques, J. F., Caseiro, R., and Batista, J. (2011). Globally optimal solution to multi-object tracking with merged measurements. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2470- 2477. IEEE.
  20. Huang, C., Wu, B., and Nevatia, R. (2008). Robust object tracking by hierarchical association of detection responses. In Computer Vision-ECCV 2008, pages 788-801. Springer.
  21. Jiang, Z., Huynh, D. Q., Moran, W., Challa, S., and Spadaccini, N. (2010). Multiple pedestrian tracking using colour and motion models. In Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on, pages 328-334. IEEE.
  22. Kaaniche, M. B. and Bremond, F. (2009). Tracking hog descriptors for gesture recognition. In Advanced Video and Signal Based Surveillance, 2009. AVSS'09. Sixth IEEE International Conference on, pages 140-145. IEEE.
  23. Kokkinos, M., Doulamis, N. D., and Doulamis, A. D. (2013). Local geometrically enriched mixtures for stable and robust human tracking in detecting falls. Int J Adv Robotic Sy, 10(72).
  24. Kosmopoulos, D. I., Doulamis, N. D., and Voulodimos, A. S. (2012). Bayesian filter based behavior recognition in workflows allowing for user feedback. Computer Vision and Image Understanding, 116(3):422- 434.
  25. Lepetit, V., Lagger, P., and Fua, P. (2005). Randomized trees for real-time keypoint recognition. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 2, pages 775-781. IEEE.
  26. Liu, C., Yuen, J., and Torralba, A. (2011). Sift flow: Dense correspondence across scenes and its applications. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(5):978-994.
  27. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  28. Lucas, B. D., Kanade, T., et al. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI, volume 81, pages 674-679.
  29. Mantiuk, R., Myszkowski, K., and Seidel, H.-P. (2006). A perceptual framework for contrast processing of high dynamic range images. ACM Trans. Appl. Percept., 3(3):286-308.
  30. Matthews, I., Ishikawa, T., and Baker, S. (2004). The template update problem. IEEE transactions on pattern analysis and machine intelligence, 26(6):810-815.
  31. Miao, Q., Wang, G., Shi, C., Lin, X., and Ruan, Z. (2011). A new framework for on-line object tracking based on surf. Pattern Recognition Letters, 32(13):1564-1571.
  32. Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. (2002). Photographic tone reproduction for digital images. In ACM Transactions on Graphics (TOG), volume 21, pages 267-276. ACM.
  33. Reinhard, E., Ward, G., Pattanaik, S., and Debevec, P. (2005). High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  34. Robertson, M. A., Borman, S., and Stevenson, R. L. (1999). Dynamic range improvement through multiple exposures. In In Proc. of the Int. Conf. on Image Processing (ICIP99, pages 159-163. IEEE.
  35. Rosten, E. and Drummond, T. (2006). Machine learning for high-speed corner detection. In Computer VisionECCV 2006, pages 430-443. Springer.
  36. Rovid, A.and Varkonyi-Koczy, A., Hashimoto, T., Balogh, S., and Shimodaira, Y. (2007). Gradient based synthesized multiple exposure time hdr image. In Instrumentation and Measurement Technology Conference Proceedings, IMTC 2007, pages 1-6.
  37. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: an efficient alternative to SIFT or SURF. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2564-2571. IEEE.
  38. Shi, J. and Tomasi, C. (1994). Good features to track. In Computer Vision and Pattern Recognition, 1994. Proceedings CVPR'94., 1994 IEEE Computer Society Conference on, pages 593-600. IEEE.
  39. Sloan, P.-P., Kautz, J., and Snyder, J. (2002). Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. In ACM Transactions on Graphics (TOG), volume 21, pages 527- 536. ACM.
  40. Stalder, S., Grabner, H., and Van Gool, L. (2009). Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pages 1409- 1416. IEEE.
  41. Tang, F., Brennan, S., Zhao, Q., and Tao, H. (2007). Cotracking using semi-supervised support vector machines. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-8. IEEE.
  42. Voulodimos, A. S., Doulamis, N. D., Kosmopoulos, D. I., and Varvarigou, T. A. (2012). Improving multicamera activity recognition by employing neural network based readjustment. Applied Artificial Intelligence, 26(1-2):97-118.
  43. Voulodimos, A. S., Kosmopoulos, D. I., Doulamis, N. D., and Varvarigou, T. A. (2014). A top-down eventdriven approach for concurrent activity recognition. Multimedia Tools and Applications, 69(2):293-311.
  44. Yu, Q., Dinh, T. B., and Medioni, G. (2008). Online tracking and reacquisition using co-trained generative and discriminative trackers. In Computer Vision-ECCV 2008, pages 678-691. Springer.
  45. Zhang, L., Li, Y., and Nevatia, R. (2008). Global data association for multi-object tracking using network flows. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
Download


Paper Citation


in Harvard Style

Agrafiotis P., Stathopoulou E., Georgopoulos A. and Doulamis A. (2015). HDR Imaging for Enchancing People Detection and Tracking in Indoor Environments . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: MMS-ER3D, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 623-630. DOI: 10.5220/0005456706230630


in Bibtex Style

@conference{mms-er3d15,
author={Panagiotis Agrafiotis and Elisavet K. Stathopoulou and Andreas Georgopoulos and Anastasios Doulamis},
title={HDR Imaging for Enchancing People Detection and Tracking in Indoor Environments},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: MMS-ER3D, (VISIGRAPP 2015)},
year={2015},
pages={623-630},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005456706230630},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: MMS-ER3D, (VISIGRAPP 2015)
TI - HDR Imaging for Enchancing People Detection and Tracking in Indoor Environments
SN - 978-989-758-090-1
AU - Agrafiotis P.
AU - Stathopoulou E.
AU - Georgopoulos A.
AU - Doulamis A.
PY - 2015
SP - 623
EP - 630
DO - 10.5220/0005456706230630