Abstract
Due to complex background information, shadow and occlusions, it is difficult to count people accurately. In this paper, we propose a fast and robust human counting approach in indoor space. Firstly, we use foreground object extraction to remove background information. In order to get both moving people and stationary people, we designed a block-updating way to update the background model. Secondly, we train a multi-view head-shoulder model to find candidate people, and an improved k-means clustering is proposed to locate the position of each people. Finally, a temporal filter with frame-difference is used to refine the counting results and detect noise, such as double-count, random disturbance. An indoor people dataset is recorded in the classroom of our university. Experiments and comparison show the promise of the proposed approach.
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
Chan, A.B., Vasconcelos, N.: Counting people with low-level features and bayesian regression. IEEE Transactions on Image Processing 21(4), 2160–2177 (2012)
Li, J., Huang, L., Liu, C.: People counting across multiple cameras for intelligent video surveillance. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 178–183 (2012)
Liu, J., Wang, J., Lu, H.: Adaptive model for robust pedestrian counting. In: Lee, K.-T., Tsai, W.-H., Liao, H.-Y.M., Chen, T., Hsieh, J.-W., Tseng, C.-C. (eds.) MMM 2011 Part I. LNCS, vol. 6523, pp. 481–491. Springer, Heidelberg (2011)
Sun, C., Zou, Q., Fu, W., Wang, J.: Multiple hypotheses based spatial-temporal association for stable pedestrian counting. In: Huet, B., Ngo, C.-W., Tang, J., Zhou, Z.-H., Hauptmann, A.G., Yan, S. (eds.) PCM 2013. LNCS, vol. 8294, pp. 803–810. Springer, Heidelberg (2013)
Teixeira, T., Savvides, A.: Lightweight people counting and localizing in indoor spaces using camera sensor nodes. In: First ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2007, pp. 36–43. IEEE (2007)
Wang, J., Fu, W., Liu, J., Lu, H.: Spatio-temporal group context for pedestrian counting. IEEE Transactions on Circuits and Systems for Video Technology, 1–11 (2014)
Zhang, E., Chen, F.: A fast and robust people counting method in video surveillance. In: 2007 International Conference on Computational Intelligence and Security, pp. 339–343. IEEE (2007)
Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II–459. IEEE (2003)
Zhao, T., Nevatia, R., Lv, F.: Segmentation and tracking of multiple humans in complex situations. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 2, pp. II–194. IEEE (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Luo, J., Wang, J., Xu, H., Lu, H. (2015). A Real-Time People Counting Approach in Indoor Environment. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-14445-0_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
eBook Packages: Computer ScienceComputer Science (R0)