Abstract
With the increasing demands on video surveillance and business promotion, effective pedestrian counting in surveillance environments has become a hot research topic in computer vision. In this paper, we implement a pedestrian counting system based on multiple object detection and tracking. Region proposal network (RPN) and Real Adaboost classifier are employed to train a head-shoulder detector with high accuracy. We utilize the DSST algorithm to track the position transformations and the size changes of pedestrians. By combining human detection with object tracking together and using detection results to optimize the tracking algorithm, the pedestrian counting system is developed with high robustness against occlusions. We evaluated the system on the videos recorded in the subway station. The results showed that our system achieves a high accuracy and can be used for pedestrian counting in crowded public places.
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This dataset can be downloaded at https://jbox.sjtu.edu.cn/l/eHE9Zh.
References
Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 613–627. Springer, Cham (2015). doi:10.1007/978-3-319-16181-5_47
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC 2009. BMVA Press (2009)
Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE PAMI 36(8), 1532–1545 (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005. vol. 1, pp. 886–893. IEEE (2005)
Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: NIPS 2014, pp. 424–432. MIT Press (2014)
Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: CVPR 2015, pp. 1751–1760. IEEE (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE PAMI 39(6), 1137–1149 (2017)
Girshick, R.: Fast R-CNN. In: ICCV 2015, pp. 1440–1448. IEEE Computer Society (2015)
Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 443–457. Springer, Cham (2016). doi:10.1007/978-3-319-46475-6_28
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR 2000, vol. 2, pp. 142–149. IEEE (2000)
Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: CVPR 2015, pp. 3119–3127. IEEE Computer Society (2015)
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: CVPR 2010, pp. 2544–2550. IEEE (2010)
Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC 2014. BMVA Press (2014)
INRIA Person Dataset. http://pascal.inrialpes.fr/data/human/
Zhang, X., Zhang, L.: Real time crowd counting with human detection and human tracking. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8836, pp. 1–8. Springer, Cham (2014). doi:10.1007/978-3-319-12643-2_1
Acknowledgement
The work was supported by the National Natural Science Foundation of China (Grant No. 91420302), the National Basic Research Program of China (Grant No. 2015CB856004) and the Key Basic Research Program of Shanghai, China (15JC1400103).
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Li, X., Zhao, H., Zhang, L. (2017). Pedestrian Counting System Based on Multiple Object Detection and Tracking. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_9
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DOI: https://doi.org/10.1007/978-3-319-70090-8_9
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