Abstract
Proxemics is a branch of anthropology that studies how humans use personal space as a means of nonverbal communication; that is, it studies how people interact. Due to the presence of physical contact between people, in the problem of proxemics recognition in images, we have to deal with occlusions and ambiguities, which complicates the process of recognition. Several papers have proposed different methods and models to solve this problem in recent years. Over the last few years, the rapid advancement of powerful Deep Learning techniques has resulted in novel methods and approaches. So, we propose Proxemics-Net, a new model that allows us to study the performance of two state-of-the-art deep learning architectures, ConvNeXt and Visual Transformers (as backbones) on the problem of classifying different types of proxemics on still images. Experiments on the existing Proxemics dataset show that these deep learning models do help favorably in the problem of proxemics recognition since we considerably outperformed the existing state of the art, with the ConvNeXt architecture being the best-performing backbone.
Supported by the MCIN Project TED2021-129151B-I00/AEI/10.13039/ 501100011033/European Union NextGenerationEU/PRTR, and project PID2019-103871GB-I00 of the Spanish Ministry of Economy, Industry and Competitiveness, FEDER.
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References
Chu, X., Ouyang, W., Yang, W., Wang, X.: Multi-task recurrent neural network for immediacy prediction. In: 2015 IEEE ICCV, pp. 3352–3360 (2015)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. In: ICLR (2021)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV 61, 55–79 (2005)
Hall, E.T.: A system for the notation of proxemic behavior. Am. Anthropol. 65(5), 1003–1026 (1963)
Jiang, H., Grauman, K.: Detangling people: individuating multiple close people and their body parts via region assembly. In: IEEE CVPR, pp. 3435–3443 (2017)
Le, V.T., Tran-Trung, K., Hoang, V.T.: A comprehensive review of recent deep learning techniques for human activity recognition. Comput. Intell. Neurosci. 2022 (2022)
Lee, D.G., Lee, S.W.: Human interaction recognition framework based on interacting body part attention. Pattern Recognit. 128 (2022)
Li, R., Porfilio, P., Zickler, T.: Finding group interactions in social clutter. In: 2013 IEEE CVPR, pp. 2722–2729 (2013)
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF CVPR (2022)
Motiian, S., Siyahjani, F., Almohsen, R., Doretto, G.: Online human interaction detection and recognition with multiple cameras. IEEE Trans. Circuits Syst. Video Technol. 27(3), 649–663 (2017)
Muhamada, A.W., Mohammed, A.A.: Review on recent computer vision methods for human action recognition. ADCAIJ 10(4), 361–379 (2021)
Ouyang, W., Zeng, X., Wang, X.: Modeling mutual visibility relationship in pedestrian detection. In: 2013 IEEE CVPR, pp. 3222–3229 (2013)
Ramanathan, V., Yao, B., Fei-Fei, L.: Social role discovery in human events. In: 2013 IEEE CVPR, pp. 2475–2482 (2013)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Vaswani, A., et al.: Attention is all you need. In: Advances in NeurIPS, vol. 30. Curran Associates, Inc. (2017)
Wu, B., et al.: Visual transformers: token-based image representation and processing for computer vision (2020)
Yang, Y., Baker, S., Kannan, A., Ramanan, D.: PROXEMICS dataset (2012). https://www.dropbox.com/s/5zarkyny7ywc2fv/PROXEMICS.zip?dl=0. Accessed 3 Mar 2023
Yang, Y., Baker, S., Kannan, A., Ramanan, D.: Recognizing proxemics in personal photos. In: 2012 IEEE CVPR, pp. 3522–3529 (2012)
Ye, Q., Zhong, H., Qu, C., Zhang, Y.: Human interaction recognition method based on parallel multi-feature fusion network. Intell. Data Anal. 25(4), 809–823 (2021)
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Jiménez-Velasco, I., Muñoz-Salinas, R., Marín-Jiménez, M.J. (2023). Proxemics-Net: Automatic Proxemics Recognition in Images. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_32
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DOI: https://doi.org/10.1007/978-3-031-36616-1_32
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