Abstract
Two important tasks in crowd analysis are crowd counting and crowd localization. In this paper, we introduce map-based crowd counting and localization methods, including density map-based methods, dot mask map-based methods, and distance transformation map-based methods. In addition, we combine the map-based methods with different losses. Finally, we compare the counting and localization performance of map-based crowd counting and localization methods on two benchmark datasets to evaluate the effectiveness of existing maps and loss functions.
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Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant No. 62171321, Natural Science Foundation of Tianjin under Grant No. 22JCQNJC00010, the Scientific Research Project of Tianjin Educational Committee under Grant No. 2022KJ011, the Tianjin Normal University Research Innovation Project for Postgraduate Students No. 2023KYCX003Z, and the Tianjin Research Innovation Project for Postgraduate Students No. 2022SKYZ048.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Lian, Y., Hu, Z., Li, X., Zhang, L., Zhang, Z., Gao, S. (2024). Learning Transformation Maps for Crowd Analysis. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_1
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DOI: https://doi.org/10.1007/978-981-99-7502-0_1
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