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Jungong Han, Eric J. Pauwels , Paul M. de Zeeuw , and Peter H.N. de With, Fellow, IEEE

Employing a RGB-D Sensor for Real-Time Tracking of Humans Across Multiple Re-Entries in a Smart Environment. Jungong Han, Eric J. Pauwels , Paul M. de Zeeuw , and Peter H.N. de With, Fellow, IEEE IEEE Transactions on Consumer Electronics , 2012. Outline. Introduction Related Works

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Jungong Han, Eric J. Pauwels , Paul M. de Zeeuw , and Peter H.N. de With, Fellow, IEEE

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  1. Employing a RGB-D Sensor for Real-Time Tracking of Humans Across Multiple Re-Entries in a Smart Environment Jungong Han, Eric J. Pauwels, Paul M. de Zeeuw, and Peter H.N. de With, Fellow, IEEE IEEE Transactions on Consumer Electronics, 2012

  2. Outline • Introduction • Related Works • Proposed Method • Experimental Results • Conclusion

  3. Introduction

  4. Introduction especially for elderly or disabledpeople. • Smart environment • Location • Identity

  5. Introduction • Smart environment • Location • Identity • Goal: Detect and track humans in a home-used system • Using a low-cost consumer-level RGB-D camera. • Combine the advantages of color and depth characteristics . especially for elderly or disabledpeople.

  6. Introduction • Requirements of home-used human tracking system • Track multiple persons • Be robust against changes in the environment • Could re-identifying persons • Real-time performance • Low-cost camera sensors

  7. Related Works

  8. Related Works • Human segmentation • RGB camera: backgroundmodeling • Median filter[3]and Gaussian Mixture Model[4] • Depth camera[10] • Motion and depth[11] • Graph cut[12]

  9. Related Works • Human tracking • RGB camera: appearance modeling • Mean shift tracker[5]: real-time non-parametric technique • Particle-filter[6]: a random search • Depth camera • Expectation Maximization algorithm[12]

  10. Related Works • RGB camera • Intuitive and easy • Depend on color/intensity =>unreliable • Depth camera • Illumination suitable • Can’t handle occlusion and identification • Both camera [13,14,15] • Proposed method

  11. Reference [3] R. Culter, and L. Davis, “View-based detection,” Proc. ICPR, 1998. [4] Z. Zivkovic, “Improved adaptive Gaussian Mixture Model for background subtraction,” Proc. ICPR, pp. 28-31, Aug. 2004. [5] D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564-577, 2003. [6] K. Nummiaro, E. Koller-Meier and L. Van Gool “An adaptive color-based particle filter” Image Vis. Comp, 2003 [10] A. Bevilacqua, L. Di and S. Azzari,“People tracking using a Time-of-Flight depth sensor”Proc. IEEE Int. Conf. Video and Signal based Surveillance, 2006 [11]D. Hansen, M. Hansen, M. Kirschmeyer, R. Larsen and D. Silvestre “Cluster tracking with Time-of-Flight cameras” Proc. CVPR workshop on TOF-CV, June, 2008 [12] O. Arif, W. Daley, P. Vela, J. Teizer and J. Stewart “Visual tracking and segmentation using Time-of-Flight sensor” Proc. ICIP, pp. 2241-2244, Sept. 2010 [13] R. Crabb, C. Tracey, A. Puranik and J. Davis “Real-time foreground segmentation via range and color imaging” Proc. CVPR Workshop on TOF-CV, June 2008 [14] S. Gould, P. Baumstarck, P. Quigley, M. Ng and A. Koller “Integrating visual and range data for robotic object detection” Proc. ECCV Workshop on Multi-camera and Multimodal Sensor Fusion, June 2008 [15] L. Sabeti, E. Parvizi and Q. Wu “Visual tracking using color cameras and Time-of-Flight range imaging sensors” Journal of multimedia, vol. 3, no. 2, pp. 28-36, June, 2008

  12. Proposed Method

  13. Proposed Method 1. 2. 3. 4.

  14. 1. Object label • Motion detection using background subtraction • B: depth of background • Depth clustering using depth information • Seeds: Moving pixels detected in the previous step • Check the depth continuity of neighboring pixels of the seeds • Returns with several separated clusters as the objects

  15. 2. Detecting Human • Problem: depend on the posture • Solution: defer computation until the person is standing • Characteristic: a moving object can be promoted to be a human only when it is stable with sufficient height. • Stable: size changes are less than 10% in 5 successive frames • Height: related with depth [16] d: distance between the camera and the object a1 and a2 : parameters in off-line calibration [16] P. Remagnino, A. Shihab, and G. Jones, “Distributed intelligence for multi-camera visual surveillance,” Pattern Recognition, vol. 37, no. 4, pp. 675-689, April 2004.

  16. 3. Human Re-Entry Identification • How? • Track persons across successive appearances in the scene • Tagpersons with a persistent ID label • Technique: appearance-based matching • Extend color histogram including color and texture • Length ratio between different body parts is fixed • Head / torso / leg

  17. 3. Human Re-Entry Identification • Head • Top 1/3 part of the entire body • Detect the length in horizontal direction • The neck width is less than others. (local minimum) • Torso and leg: by ratio

  18. 3. Human Re-Entry Identification • Use color histogram and texture to describe the human appearance • Probability of feature u: • : the pixel locations in the defined region • : associates to the pixel at location Formula the index • C: normalization constant • the Kroneckerdelta function • Texture intensity using canny detection

  19. 3. Human Re-Entry Identification • Compare two histogram as similarity

  20. 4.Human ID Tracking • Depth continuity • Gaussian distribution: • Appearance similarity • Bhattacharyya distance: • The Probability Ti matching with Dj

  21. Proposed Method 1. 2. 3. 4.

  22. Experimental Results

  23. Experimental Results • Device: Dual core 2.53 GHz, 4 GB RAM with a 64-bits operation system • Implemented by C++ with OpenNI and OpenCV library • Evaluation • A. Object labeling • B. Human detection and ID tracking • C. System efficiency

  24. Experimental Results A. • Object labeling in different situation Stable light Similar color between F & B Stable light Different color between foreground & background

  25. Using depth data  Using RGB data 

  26. Experimental Results B. • Human region detection • 96.1% accuracy in 2000 frames • Only 78 frames failed

  27. Experimental Results B. • Identification • Set the RGB-D sensor in a living room for 30 minutes, and asked persons to leave and come back for 35 times.  • The persons used 5 different coats.  • Results: only 8 occasions fail • Coats with similar colors • Posture difference

  28. Experimental Results B. • Accuracy in human tracking module(based on 5 videos ,in total 2600 frames) • Proposed: 96.27% • Particle filter[6]: 83.54% =>illumination • Mean shift filter[5]: 71.23% =>occlusion [5] D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,”IEEETrans. Pattern Anal. Mach. Intell., 2003. [6] K. Nummiaro, E. Koller-Meier and L. Van Gool “An adaptive color-based particle filter” Image Vis. Comp, 2003

  29. Experimental Results B. 1 1 0 0 0 0 1 1 Anonym Anonym

  30. Experimental Results C. • System efficiency in 100 frames • 1-person: 41.3 ms/frame • 2-person: 73.8 ms/frame • 3-person: 97.1 ms/frame • Overall about more than10 fps

  31. Conclusion

  32. Conclusion • Proposed a two-camera system based on a RGB-D sensor, which enables person detection, tracking and re-entryidentification. • Proposed system can achieve real-time performance with sufficient accuracy • 95% detection; 80% re-identification; 96% occlusion and illumination • Future Work • Improve human detector to execute with a more general descriptor 

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