Rimboux, A., Dupre, R., Lagkas, T. orcid.org/0000-0002-0749-9794 et al. (3 more authors) (2019) Smart IoT cameras for crowd analysis based on augmentation for automatic pedestrian detection, simulation and annotation. In: Proceedings of 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), 29-31 May 2019, Santorini Island, Greece. IEEE , pp. 304-311. ISBN 9781728105710
Abstract
Smart video sensors for applications related to surveillance and security are IOT-based as they use Internet for various purposes. Such applications include crowd behaviour monitoring and advanced decision support systems operating and transmitting information over internet. The analysis of crowd and pedestrian behaviour is an important task for smart IoT cameras and in particular video processing. In order to provide related behavioural models, simulation and tracking approaches have been considered in the literature. In both cases ground truth is essential to train deep models and provide a meaningful quantitative evaluation. We propose a framework for crowd simulation and automatic data generation and annotation that supports multiple cameras and multiple targets. The proposed approach is based on synthetically generated human agents, augmented frames and compositing techniques combined with path finding and planning methods. A number of popular crowd and pedestrian data sets were used to validate the model, and scenarios related to annotation and simulation were considered.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | crowd analysis; data augmentation; crowd behavior |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > International Faculty (Sheffield) > City College - Computer Science |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jul 2019 10:31 |
Last Modified: | 19 Aug 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/DCOSS.2019.00070 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148173 |