Abstract
A robust and reliable person detection is crucial for many applications. In the domain of service robots that we focus on, knowing the location of a person is an essential requirement for any meaningful human-robot interaction. In this work we present a people detection algorithm exploiting RGB-D data from Kinect-like cameras. Two features are obtained from the data representing the geometrical properties of a person. These features are transformed into the frequency domain using Discrete Fourier Transform (DFT) and used to train a Support Vector Machine (SVM) for classification. Additionally, we present a hand detection algorithm based on the extracted silhouette of a person. We evaluate the proposed method on real world data from the Cornell Activity Dataset and on a dataset created in our laboratory.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bertozzi, M., Broggi, A., Del Rose, M., Felisa, M., Rakotomamonjy, A., Suard, F.: A pedestrian detector using histograms of oriented gradients and a support vector machine classifier. In: Intelligent Transportation Systems Conference, ITSC 2007, pp. 143–148. IEEE (2007)
Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys (CSUR) 24(4), 325–376 (1992)
Cerlinca, T.L., Pentiuc, S.G., Vatavu, R.D., Cerlinca, M.C.: Hand posture recognition for human-robot interaction. In: Proceedings of the 2007 Workshop on Multimodal Interfaces in Semantic Interaction, pp. 47–50. ACM (2007)
Choi, W., Pantofaru, C., Savarese, S.: Detecting and tracking people using an rgb-d camera via multiple detector fusion. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1076–1083. IEEE (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (June 2005)
Ghosh, S., Zheng, J., Chen, W., Zhang, J., Cai, Y.: Real-time 3d markerless multiple hand detection and tracking for human computer interaction applications. In: Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry, pp. 323–330. ACM (2010)
González, D.I.R., Hayet, J.-B.: Fast Human Detection in RGB-D Images with Progressive SVM-Classification. In: Klette, R., Rivera, M., Satoh, S. (eds.) PSIVT 2013. LNCS, vol. 8333, pp. 337–348. Springer, Heidelberg (2014)
Hordern, D., Kirchner, N.: Robust and efficient people detection with 3-d range data using shape matching. In: Australasian Conference on Robotics and Automation (2010)
Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognition 40(3), 1106–1122 (2007)
Lee, S.J., Nguyen, D.D., Jeon, J.W.: Design and Implementation of Depth Image Based Real-Time Human Detection. Journal of Semiconductor Technology and Science 14(2), 212–226 (2014)
Xia, L., Chen, C.-C., Aggarwal, J.K.: Human Detection Using Depth Information by Kinect. In: International Workshop on Human Activity Understanding from 3D Data in Conjunction with CVPR (HAU3D) (2011)
Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features. CoRR, abs/1407.0786 (2014)
Spinello, L., Arras, K.O.: People detection in RGB-D data. In: IEEE/RSJ Int. Conf. on (2011)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from rgbd images. plan, activity, and intent recognition, 64 (2011)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Seib, V., Schmidt, G., Kusenbach, M., Paulus, D. (2015). Fourier Features For Person Detection in Depth Data. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_69
Download citation
DOI: https://doi.org/10.1007/978-3-319-23192-1_69
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23191-4
Online ISBN: 978-3-319-23192-1
eBook Packages: Computer ScienceComputer Science (R0)