Smart IoT cameras for crowd analysis based on augmentation for automatic pedestrian detection, simulation and annotation

Rimboux, Antoine, Dupre, Rob, Daci, Eldriona, Lagkas, Thomas, Sarigiannidis, Panagiotis, Remagnino, Paolo and Argyriou, Vasileios (2019) Smart IoT cameras for crowd analysis based on augmentation for automatic pedestrian detection, simulation and annotation. In: 15th Annual International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019; 29-31 May 2019, Santorini Island, Greece.

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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.

Item Type: Conference or Workshop Item (Paper)
Event Title: 15th Annual International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019
Additional Information: This work is co-funded by the NATO within the WITNESS project under grant agreement number G5437. The Titan X Pascal used for this research was donated by NVIDIA. Published in: 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). Institute of Electrical and Electronics Engineers, Inc. ISSN (online) 2325-2944 ISBN 9781728105703
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
Depositing User: Philip Keates
Date Deposited: 26 Feb 2020 11:56
Last Modified: 26 Feb 2020 11:56
DOI: https://doi.org/10.1109/DCOSS.2019.00070
URI: http://eprints.kingston.ac.uk/id/eprint/44031

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