Abstract
The recognition of people orientation in single images is still an open issue in several real cases, when the image resolution is poor, body parts cannot be distinguished and localized or motion cannot be exploited. However, the estimation of a person orientation, even an approximated one, could be very useful to improve people tracking and re-identification systems, or to provide a coarse alignment of body models on the input images. In these situations, holistic features seem to be more effective and faster than model based 3D reconstructions. In this paper we propose to describe the people appearance with multi-level HoG feature sets and to classify their orientation using an array of Extremely Randomized Trees classifiers trained on quantized directions. The outputs of the classifiers are then integrated into a global continuous probability density function using a Mixture of Approximated Wrapped Gaussian distributions. Experiments on the TUD Multiview Pedestrians, the Sarc3D, and the 3DPeS datasets confirm the efficacy of the method and the improvement with respect to state of the art approaches.
Chapter PDF
Similar content being viewed by others
References
Andriluka, M., Roth, S., Schiele, B.: Monocular 3d pose estimation and tracking by detection. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 623–630 (2010)
Ferrari, V., Marín-Jiménez, M., Zisserman, A.: 2D Human Pose Estimation in TV Shows. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Visual Motion Analysis. LNCS, vol. 5604, pp. 128–147. Springer, Heidelberg (2009)
Calderara, S., Prati, A., Cucchiara, R.: Mixtures of von mises distributions for people trajectory shape analysis. IEEE Trans. Circuits Syst. Video Technol. 21, 457–471 (2011)
Chen, C., Heili, A., Odobez, J.: Combined estimation of location and body pose in surveillance video. In: Proc. of IEEE Conf. on Advanced Video and Signal-Based Surveillance, pp. 5–10 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE Computer Society, Washington, DC (2005)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 63, 3–42 (2006)
Baltieri, D., Vezzani, R., Cucchiara, R.: Sarc3d: a new 3d body model for people tracking and re-identification. In: Proc. of IEEE Int. Conf. on Image Anal. and Process., Ravenna, Italy, pp. 197–206 (2011)
Baltieri, D., Vezzani, R., Cucchiara, R.: 3dpes: 3d people dataset for surveillance and forensics. In: Proc. of the 1st International ACM Workshop on Multimedia access to 3D Human Objects, Scottsdale, Arizona, USA, pp. 59–64 (2011)
Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Pose search: Retrieving people using their pose. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2009)
Lanz, O., Brunelli, R.: Dynamic head location and pose from video. In: IEEE Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp. 47–52 (2006)
Canton-Ferrer, C., Casas, J.R., Pardàs, M.: In: Head Orientation Estimation Using Particle Filtering in Multiview Scenarios, pp. 317–327. Springer, Heidelberg (2008)
Gourier, N., Hall, D., Crowley, J.L.: Estimating Face Orientation from Robust Detection of Salient Facial Features. In: Proceedings of Pointing 2004, International Workshop on Visual Observation of Deictic Gestures, ICPR (2004)
Setthawong, P., Vannija, V.: Improving the estimation of head pose orientation: By using eyeglasses as a key feature. In: Proc. of Int. Conf. on Information Technology and Multimedia (ICIM 2011), pp. 1–6 (2011)
Ozturk, O., Yamasaki, T., Aizawa, K.: Estimating human body and head orientation change to detect visual attention direction. In: Proc. IEEE Int. Conf. Comput. Vision, ACCV 2010, pp. 410–419. Springer, Heidelberg (2011)
Huang, C., Ding, X., Fang, C.: Head pose estimation based on random forests for multiclass classification. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 934–937 (2010)
Cristani, M., Bazzani, L., Pagetti, G., Fossati, A., Tosato, D., Del Bue, A., Menegaz, G., Murino, V.: Social interaction discovery by statistical analysis of f-formations. In: British Machine Vision Conference, BMVC (2011)
Chen, C., Heili, A., Odobez, J.M.: A joint estimation of head and body orientation cues in surveillance video. In: Proc. IEEE Int. Conf. Comput. Vision Workshops, pp. 860–867 (2011)
Rogez, G., Rihan, J., Ramalingam, S., Orrite, C., Torr, P.: Randomized trees for human pose detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 304–311 (2009)
Mardia, K.V.: Statistics of directional data. Journal of the Royal Statistical Society. Series B (Methodological) 37, 349–393 (1975)
Agiomyrgiannakis, Y., Stylianou, Y.: Wrapped gaussian mixture models for modeling and high-rate quantization of phase data of speech. IEEE Trans. on Audio Speech And Language Processing 17, 775–786 (2009)
Bahlmann, C.: Directional features in online handwriting recognition. Pattern Recognition 39, 115–125 (2006)
Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1713–1727 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baltieri, D., Vezzani, R., Cucchiara, R. (2012). People Orientation Recognition by Mixtures of Wrapped Distributions on Random Trees. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33715-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-33715-4_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33714-7
Online ISBN: 978-3-642-33715-4
eBook Packages: Computer ScienceComputer Science (R0)