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https://hdl.handle.net/2440/129211
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Type: | Conference paper |
Title: | Joint learning of social groups, individuals action and sub-group activities in videos |
Author: | Ehsanpour, M. Abedin Varamin, A. Saleh, F. Shi, Q. Reid, I.D. Rezatofighi, H. |
Citation: | Lecture Notes in Artificial Intelligence, 2020 / Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (ed./s), vol.12354, pp.177-195 |
Publisher: | Springer |
Publisher Place: | Cham, Switzerland |
Issue Date: | 2020 |
Series/Report no.: | Lecture Notes in Computer Science; 12354 |
ISBN: | 3030585441 9783030585440 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 16th European Conference on Computer Vision Workshops (ECCV) (23 Aug 2020 - 28 Aug 2020 : Glasgow, UK) |
Editor: | Vedaldi, A. Bischof, H. Brox, T. Frahm, J.-M. |
Statement of Responsibility: | Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh, Javen Shi, Ian Reid, and Hamid Rezatofighi |
Abstract: | The state-of-the art solutions for human activity understanding from a video stream formulate the task as a spatio-temporal problem which requires joint localization of all individuals in the scene and classification of their actions or group activity over time. Who is interacting with whom, e.g. not everyone in a queue is interacting with each other, is often not predicted. There are scenarios where people are best to be split into sub-groups, which we call social groups, and each social group may be engaged in a different social activity. In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task. Our main contributions are: i) we propose an end-to-end trainable framework for the social task; ii) our proposed method also sets the state-of-the-art results on two widely adopted benchmarks for the traditional group activity recognition task (assuming individuals of the scene form a single group and predicting a single group activity label for the scene); iii) we introduce new annotations on an existing group activity dataset, re-purposing it for the social task. The data and code for our method is publicly available (https://github.com/mahsaep/Social-human-activity-understanding-and-grouping). |
Keywords: | Collective behaviour recognition; Social grouping; Video understanding |
Rights: | © Springer Nature Switzerland AG 2020 |
DOI: | 10.1007/978-3-030-58545-7_11 |
Published version: | https://doi.org/10.1007/978-3-030-58545-7 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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