Abstract
This paper proposes a people-flow counting algorithm using top-view depth images for implementation on low-power, embedded processors. In the people detection stage the algorithm uses morphological connected filters to find head candidates, and in the tracking stage it uses Kalman filtering in order to obtain good predictions in frames where detection fails. A fast interpolation algorithm is also proposed, which estimates the values of pixels affected by noise and generates an image with a continuous domain. The experiments were done using a Kinect sensor and the processing was performed in real time on a Raspberry Pi 3. The dataset consisted of 4025 short video sequences of people entering and exiting indoor environments, obtained from three different installations. The algorithm proved to be adequate for an embedded application, reaching an accuracy of 98% for frame rates as low as 5.5 FPS.
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References
Bernini, N., Bombini, L., Buzzoni, M., Cerri, P., Grisleri, P.: An embedded system for counting passengers in public transportation vehicles. In: 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6, September 2014
Bondi, E., Seidenari, L., Bagdanov, A.D., Del Bimbo, A.: Real-time people counting from depth imagery of crowded environments. In: 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 337–342. IEEE (2014)
Borgefors, G.: Distance transformations in digital images. Comput. Vis. Graph. Image Process. 34(3), 344–371 (1986)
Burbano, A., Bouaziz, S., Vasiliu, M.: 3D-sensing distributed embedded system for people tracking and counting. In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 470–475, December 2015
Dougherty, E.R., Lotufo, R.A.: Hands-on Morphological Image Processing. SPIE Tutorial Texts in Optical Engineering, vol. TT59. SPIE Publications, Bellingham (2003)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)
Malawski, F.: Top-view people counting in public transportation using Kinect. Chall. Mod. Technol. 5, 17–20 (2014)
Sgouropoulos, D., Spyrou, E., Siantikos, G., Giannakopoulos, T.: Counting and tracking people in a smart room: an IoT approach. In: 2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 1–5, November 2015
Yang, D.B., Gonzlez-Baos, H.H., Guibas, L.J.: Counting people in crowds with a real-time network of simple image sensors. In: 2003 Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 122–129. IEEE (2003)
Zhang, E., Chen, F.: A fast and robust people counting method in video surveillance, pp. 339–343. IEEE, December 2007
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Soares, G.S., Machado, R.C., Lotufo, R.A. (2017). People-Flow Counting Using Depth Images for Embedded Processing. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_27
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DOI: https://doi.org/10.1007/978-3-319-59876-5_27
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