Conteo de personas con un sensor RGBD comercial

M. Castrillón Santana, J. Lorenzo Navarro, D. Hernández Sosa

Resumen

En este trabajo se demuestra que la información de profundidad proporcionada por una cámara RGBD comercial de bajo coste, es una fuente fiable de datos para realizar de forma robusta el conteo automático de personas. La adopción de una configuración de vista cenital reduce la complejidad del problema, al mismo tiempo que permite preservar la privacidad de las personas moni- torizadas. Para llevar a cabo el estudio experimental se han considerado dos técnicas propias del campo de análisis de imágenes 2D trasladadas al contexto de imágenes de profundidad. Las pruebas evaluaron su rendimiento con v́ıdeos reales sin restricciones de iluminación, incluyendo episodios de iluminación cambiante o muy baja. En este conjunto experimental se realizó la detección, seguimiento y análisis de patrones de comportamiento de las personas que cruzaban el campo de visión. Los resultados obtenidos alcanzan una tasa de acierto próxima al 95%, superando los obtenidos con técnicas actuales basadas exclusivamente en información visual. Estos resultados sugieren la utilidad del uso de información de profundidad en esta tarea particular.

Palabras clave

Conteo de personas; cámaras de profundidad; detección de eventos; detección de objetos

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Referencias

Albiol, A., Albiol, A., Oliver, J., J. M., September 2012. Who is who at different cameras: people re-identification using depth cameras. IET Computer Vision 6 (5), 378–387.

Albiol, A., Mora, I., Naranjo, V., December 2001. Real-time high density people counter using morphological tools. IEEE Transactions on Intelligent Transportation Systems 2 (4), 204–218.

Albiol, A., Silla, J., 2010. Statistical video analysis for crowds counting. En: Proceedings of the 16th IEEE international conference on Image Processing (ICIP). pp. 2569–2572.

Andriluka, M., Roth, S., Schiele, B., 2008. People-tracking-by-detection and people-detection-by-tracking. En: IEEE Conf. on Computer Vision and Pattern Recognition.

Antic, B., Letic, D., D. Culibrk, V. C., 2010. K-means based segmentation for real-time zenithal people counting. En: Proceedings of the 16th IEEE International Conference on the Image Processing (ICIP). pp. 2565–2568.

Barandiaran, J., Murguia, B., Boto, F., 2008. Real-time people counting using multiple lines. En: Ninth International Workshop on Image Analysis for Multimedia Interactive Services. pp. 159–162.

Barbosa, B. I., Cristani, M., Bue, A. D., Bazzani, L., Murino, V., 2012. Reidentification with RGB-D sensors. En: 1st International Workshop on ReIdentification.

Bellotto, N., Hu, H., Feb- 2009. Multisensor-based human detection and tracking for mobile service robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 (1), 167–181.

Beymer, D., 2000. Person counting using stereo. En: Workshop on Human Motion. pp. 127–133.

Blanco, J., Burgard, W., Sanz, R., Fernandez, J., 2003. Fast face detection for mobile robots by integrating laser range data with vision. En: Proc. of the International Conference on Advanced Robotics (ICAR). pp. 953–958.

Bozzoli, M., Cinque, L., Sangineto, E., 2007. A statistical method for people counting in crowded environments. En: 14th International Conference on Image Analysis and Processing.

Brutzer, S., Hoferlin, B., Heidemann, G., 2011. Evaluation of background subtraction techniques for video surveillance. En: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1937–1944.

Camplani, M., del Blanco, C. R., Salgado, L., Jaureguizar, F., Garc´ı, N., January 2014. Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction. Machine Vision and Applications 25 (1), 122–136.

Chan, A. B., Liang, Z.-S. J., Vasconcelos, N., 2008. Privacy preserving crowd monitoring: Counting people without people models or tracking. En: Computer Vision and Pattern Recognition. pp.1–7.

Chan, A. B., Vasconcelos, N., April 2012. Counting people with low-level features and bayesian regression. IEEE TRANSACTIONS ON IMAGE PROCESSING 21 (4), 2160–2177.

Cohen, I., Garg, A., Huang, T., 2000. Vision-based overhead view person recognition. En: 15th International Conference on Pattern Recognition.

Cui, J., Zha, H., Zhao, H., Shibasaki, R., 2007. Laser-based detection and tracking of multiple people in crowds. Computer Vision and Image Understanding 106, 300–312.

Cui, J., Zha, H., Zhao, H., Shibasaki, R., 2008. Multi-modal tracking of people using laser scanners and video camera. Image and Vision Computing 26 (2), 240–252.

Englebienne, G., Krose., B., 2010. Fast bayesian people detection. En: 22nd Benelux Conference on Artificial intelligence.

Englebienne, G., van Oosterhout, T., Krose, B., 2009. Tracking in sparse multicamera setups using stereo vision. En: Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

Fanelli, G., Gall, J., Gool, L. V., 2011a. Real time head pose estimation with random regression forests. En: Computer Vision and Patter Recognition (CVPR).

Fanelli, G., Weise, T., Gall, J., Gool, L. V., 2011b. Real time head pose estimation from consumer depth cameras. En: 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM).

Fod, A., Howard, A., Mataric, M. J., May 2002. Laser-based people tracking. En: IEEE International Conference on Robotics and Automation (ICRA). Washington D.C., pp. 3024–3029.

García, J., Gardel, A., Bravo, I., Lázaro, J. L., Martínez, M., Rodríguez, D., Octubre-Diciembre 2012. Detección y seguimiento de personas basado en estereovisión y filtro de kalman. Revista Iberoamericana de Automática e Informática Industrial 9 (4).

Gollan, B., Wally, B., Ferscha, A., 2011. Id management strategies for interactive systems in multi-camera scenarios. En: 4th Conference on Context Awareness for Proactive Systems (CAPS). Budapest.

Han, B., Comaniciu, D., Zhu, Y., Davis, L., 2004. Incremental density approximation and kernel-based bayesian filtering for object tracking. En: IEEE Conf. on Computer Vision and Pattern Recognition. pp. 638–644.

Harville, M., 2004. Stereo person tracking with adaptive plan-view templates of height and occupancy statistics. Image and Vision Computing 22 (2), 127– 142.

Heikkila, J., Silven, O., June 1999. A real-time system for monitoring of cyclists and pedestrians. En: IEEE Workshop on Visual Surveillance. Fort Collins, Colorado, pp. 82–90.

Hernández, D., Castrillón, M., Lorenzo, J., 2011. People counting with re-identification using depth cameras. En: 4th International Conference on Imaging for Crime Detection and Prevention (ICDP).

Hernández-Sosa, D., Castrillón-Santana, M., Lorenzo-Navarro, J., 2011. Multisensor people counting. En: IbPRIA. pp. 321–328.

Hou, Y., Pang, G., 2011. People counting and human detection in a challenging situation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 41, 24–33.

Katabira, K., Nakamura, K., Zhao, H., Shibasaki, R., November 22 - 26 2004. A method for counting pedestrians using a laser range scanner. En: 25th Asian Conference on Remote Sensing (ACRS 2004). Thailand.

Kim, J. W., Choi, K. S., Park, W.-S., Lee, J.-Y., Ko, S. J., September 2002. Robust real-time people tracking system for security. Intelligent Building Society (IBS) 2 (3), 184 – 190.

Lee, G.-G., ki Kim, H., Yoon, J.-Y., Kim, J.-J., Kim, W.-Y., 2008. Pedestrian counting using an IR line laser. En: International Conference on Convergence and Hybrid Information Technology 2008.

Leibe, B., Schindler, K., Cornelis, N., Gool: L. J. V., 2008. Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. 30 (10), 1683–1698.

Lorenzo-Navarro, J., Castrillón-Santana, M., Hernández-Sosa, D., 2013. On the use of simple geometric descriptors provided by RGB-D sensors for reidentification. Sensors 13 (7), 8222–8238.

Marcos, A., Pizarro, D., Marrón, M., Mazo, M., Abril 2013. Captura de movimiento y reconocimiento de actividades para múltiples personas mediante un enfoque bayesiano. Revista Iberoamericana de Automática e Informática Industrial 10 (2).

Mathews, E., Poigné, A., 2009. Evaluation of a ”smart”pedestrian counting system based on echo state networks. EURASIP Journal on Embedded Systems 2009, 1–9. DOI: http://dx.doi.org/10.1155/2009/352172

Moore, B. E., Ali, S., Mehran, R., Shah, M., December 2011. Visual crowd surveillance through a hydrodynamics lens. Communications of the ACM 54 (12), 64–73.

Nakamura, K., Zhao, H., Shibasaki, R., K.S., Ohga, T., Suzukawa, N., 2006. Tracking pedestrians using multiple single-row laser range scanners and its reliability evaluation,. Systems and Computers in Japan 37, 1–11.

Oliver, J., Albiol, A., Albiol, A., 2012. 3d descriptor for people re-identification. En: 21st International Conference on Pattern Recognition (ICPR).

Piccardi, M., 2004. Background subtraction techniques: a review. En: IEEE International Conference on Systems, Man and Cybernetics. pp. 3099–3104.

Qiuyu, Z., Li, T., Yiping, J., Wei-jun, D., 2010. A novel approach of counting people based on stereovision and dsp. En: The 2nd International Conference on Computer and Automation Engineering (ICCAE).

Satta, R., Pala, F., Fumera, G., Roli, F., 2013. Real-time appearance-based person re-identification over multiple kinect cameras. En: 8th International Conference on Computer Vision Theory and Applications (VISAPP). Barcelona, Spain.

Scheutz, M., McRaven, J., Cserey, G., 2004. Fast, reliable, adaptive, bimodal people tracking for indoor environments. En: Proceedings. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004). Vol. 2. pp. 1347–1352.

Septian, H., Tao, J., Tan, Y.-P., 5-8 Dec. 2006 2006. People counting by video segmentation and tracking. En: 9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV ’06. Singapore, pp. 1–4.

Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipma, A., Blake, A., June 2011. Real-time human pose recognition in parts from a single depth image. En: Computer Vision and Pattern Recognition.

Spinello, L., Arras, K. O., 2011. People detection in RGB-D data. En: Proc. of The International Conference on Intelligent Robots and Systems (IROS).

Stauffer, Grimson, 1999. Adaptive background mixture models for real-time tracking. En: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 246–252.

van Oosterhout, T., Bakkes, S., Krose, ¨ B., 2011. Head detection in stereo data for people counting and segmentation. En: International Conference on Computer Vision Theory and Applications (VISAPP). pp. 620–625.

Velipasalar, S., li Tian, Y., Hampapur, A., July 2006. Automatic counting of interacting people by using a single uncalibrated camera. En: IEEE International Conference on Multimedia and Expo. Toronto, ON, Canada.

Xia, L., Chen, C.-C., Aggarwal, J. K., June 2011. Human detection using depth information by kinect. En: International Workshop on Human Activity Understanding from 3D Data in conjunction with CVPR (HAU3D). Colorado Springs, CO.

Yahiaoui, T., Khoudour, L., Meurie, C., July 2010. Real-time passenger counting in buses using dense stereovision. J. Electron. Imaging 20.

Yu, H., Liu, J., Liu, J., 2007. 3d feature extraction of head based on target region matching. En: Proceedings of the International Conference on Computational Intelligence and Security. pp. 366–370.

Zeng, C., Ma, H., 2010. Robust head-shoulder detection by pca-based multilevel HOG-LBP detector for people counting. En: 20th International Conference on Pattern Recognition (ICPR). Istambul, pp. 2069–2072.

Zhan, B., Monekosso, D. N., Remagnino, P., Velastin, S. A., Xu, L.-Q., 2008. Crowd analysis: a survey. Machine Vision and Applications 19, 345–357.

Zhao, X., Dellandréa, E., Chen, L., 2009. A people counting system based on face detection and tracking in a video. En: Advanced Video and Signal Based Surveillance.

Zivkovic, Z., der Heijden, F., 2006. Effcient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters 27, 773–780.

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PLOS ONE  vol: 11  num.: 2  primera página: e0149665  año: 2016  
doi: 10.1371/journal.pone.0149665



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