Publicado

2017-01-01

Pedestrian tracking using probability fields and a movement feature space

Seguimiento de peatones utilizando campos probabilísticos y un espacio de descriptores dinámicos

Palabras clave:

pedestrian tracking, movement feature space, target framework (en)
seguimiento de peatones, espacio de descriptores dinámicos, target framework (es)

Autores/as

Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajectory and speed by using a state machine. The particularity of our methodology is the use of a Movement Feature Space (MFS) to generate descriptors for classifiers and trackers. This approach is applied to two public sequences (PETS2009 and TownCentre). The results of this tracking outperform other algorithms reported in the literature, which have, however, a higher computational complexity.
Recuperar información de secuencias de video, como la dinámica de peatones u otros objetos en movimiento en la escena, representa una herramienta indispensable para interpretar que está ocurriendo en la escena. Este artículo propone el uso de una Arquitectura basada en Targets, que asocian a cada persona una entidad autónoma y modeliza su dinámica con una máquina de estados. Nuestra metodología utiliza una familia de descriptores calculados en el Movement Feature Space (MFS) para realizar la detección y seguimiento de las personas. Esta arquitectura fue evaluada usando dos bases de datos públicas (PETS2009 y TownCentre), y comparándola con algoritmos de la literatura, arrojó mejores resultados, aun cuando estos algoritmos poseen una mayor complejidad computacional.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Smeulders, A.W., Chu, D., Cucchiara, R., Calderara, S., Dehghan, A. and Shah, M., Visual tracking: An experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(7), pp. 1442-1468, 2014. DOI: 10.1109/TPAMI.2013.230

Goldhammer, M., Gerhard, M., Zernetsch, S., Doll, K. and Brunsmann, U., Early prediction of a pedestrians trajectory at intersections. IEEE Proceedings on Intelligent Transport Systems. pp. 237-242, 2013. DOI: 10.1109/ITSC.2013.6728239

Zhang, Y., Ji, Q. and Lu, H., Event detection in complex scenes using interval temporal constraints. In: IEEE International Conference on Computer Vision. pp. 3184-3191, 2013. DOI: 10.1109/ICCV.2013.395

Jodoin, J., Bilodeau, G. and Saunier, N., Urban tracker: Multiple object tracking in urban mixed traffic. In: IEEE Winter Conf. on App. of Comp. Vision. pp. 885-892, 2014. DOI: 10.1109/WACV.2014.6836010

Keller, C. and Gavrila, D., Will the pedestrian cross?. A study on pedestrian path prediction. IEEE Trans. on Intell. Transp. Systems 15(2), pp. 494-506, 2014. DOI: 10.1109/TITS.2013.2280766

Lucas, B. and Kanade, T., An iterative image registration technique with an application to stereo vision. In: Proc. Int. Joint Conf. on Artificial Intell. 2, pp. 674-679, August, 1981.

Shi, J. and Tomasi, C., Good features to track. In: IEEE Proc. Comp. Vis. and Pattern Recognit. pp. 593-600, June, 1994. DOI: 10.1109/CVPR.1994.323794

Comaniciu, D., Ramesh, V. and Meer, P., Real-time tracking of non-rigid objects using mean shift. In: IEEE Proc. Comp. Vis. and Pattern Recognit. 2, pp. 142-149, 2000. DOI: 10.1109/CVPR.2000.854761

Kalal, Z., Mikolajczyk, K. and Matas, J., Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), pp. 1409-1422, 2012. DOI: 10.1109/TPAMI.2011.239

Dalal, N. and Triggs, B., Histograms of oriented gradients for human detection. In: IEEE Proc. Comp. Vis. and Pattern Recognit.1, pp. 886-893, 2005. DOI: 10.1109/CVPR.2005.177

OpenCV library v. 2.4.9.0, [online]. [Accessed April 2016]. Available at: http://opencv.org/

Zhang, J., Presti, L. and Sclaroff, S., Online multi-person tracking by tracker hierarchy. In: Proc. Int. Conf. on Adv. Video and Signal-Based Surveillance. pp. 379-385, September, 2012. DOI: 10.1109/AVSS.2012.51

Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E. and Van Gool, L., Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), pp. 1820-1833, 2011. DOI: 10.1109/TPAMI.2010.232

Leibe, B., Schindles, K., Cornelis, N. and Van Gool, L., Coupled object detection and tracking form static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell., 30(10), pp. 1683-1698, 2008.

Ben-Shitrit, H., Berclaz, J., Fleuret, F. and Fua, P., Tracking multiple people under global appearance constraints. IEEE Int. Conf. on Comp. Vision. pp. 137-144, 2011. DOI: 10.1109/ICCV.2011.6126235

Milan, A., Roth, S. and Schindler, K., Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell., 36(1), pp. 58-72, 2014. DOI: 10.1109/TPAMI.2013.103

Negri, P., Estimating the queue length at street intersections by using a movement feature space approach. IET Image Processing 8, pp. 406-416, 2014. DOI: 10.1049/iet-ipr.2013.0496

Negri, P., Goussies, N. and Lotito, P., Detecting pedestrians on a movement feature space. Pattern Recognition 47(1), pp. 56-71, 2014. DOI: 10.1016/j.patcog.2013.05.020

Everingham, M., Gool, L., Williams, C.K., Winn, J. and Zisserman, A., The PASCAL Visual Object Classes (VOC) Challenge. Int. J. Comp. Vis. 8(2), pp. 303-338, 2010.

Deriche, R. and Faugeras, O.D., Tracking line segments. In: European Conf. on Comp. Vis. pp. 259-268, 1990. DOI: 10.1016/0262-8856(90)80002-B

Negri, P. and Garayalde, D., Concatenating multiple trajectories using kalman filter for pedestrian tracking. In: IEEE Biennial Cong. of Argentina. pp. 364-369, 2014. DOI: 10.1109/ARGENCON.2014.6868520

PETS2009. [online]. [Accessed on June 2016], Available at: http://www.cvg.reading.ac.uk/PETS2009/a.html

Benfold, B. and Reid, I., Stable multi-target tracking in real-time surveillance video. In: IEEE Proc. Comp. Vis. Pattern Recognit., pp. 3457-3464, 2011. DOI: 10.1109/CVPR.2011.5995667

Town Centre video and data, [online]. [Accessed June 2016], Available at http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html

Negri, P., Clady, X. and Prevost, L., Benchmarking haar and histograms of oriented gradients features applied to vehicle detection. In: Int. Conf. on Informat. in Control, Automat. and Robotics. pp. 359-364, 2007.

Negri, P., Clady, X., Hanif, S. and Prevost, L., A cascade of boosted generative and discriminative classifiers for vehicle detection. EURASIP JASP 2008, pp. 1-12, 2008. DOI: 10.1155/2008/782432.

Pedestrian Patches-PANKit. [online]. [Accessed on June 2016]. Available at http://pablonegri.free.fr/Downloads/PedestrianPatchesDataset-PANKit.htm

PASCAL VOC2012 dataset. [online]. [Accessed June 2016], Available at (register required) http://host.robots.ox.ac.uk:8080/.

Online multi-person tracking by tracker hierarchy code. [online]. [Accessed June 2016]. Available at: http://cs-people.bu.edu/jmzhang/tracker_hierarchy/Tracker_Hierarchy.htm

Continuous energy minimization for multi-target tracking matlab code. [online]. [Accessed on June 2016]. Available at: http://www.milanton.de/contracking/

Negri, P., and Lotito, P., Pedestrian detection using a feature space based on colored level lines. In: Alvarez, L., Mejail, M., Gomez, L. and Jacobo, J. (Eds). Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, 7441, pp 885-892, Springer, Berlin, Heidelberg, 2012. DOI: 10.1007/978-3-642-33275-3_109.