Learning how to analyse crowd behaviour using synthetic data

Khadka, Anish; Oghaz, Mahdi Maktab Dar; Matta, Walter; Cosentino, Marco; Remagnino, Paolo; Argyriou, Vasileios

doi:10.1145/3328756.3328773

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…The second method is to use 2D overlays to project images of humans onto a 2D background. A paper on large crowd analysis using synthetic data [23] projected synthetic humans onto real scenes. The synthesis enabled illumination, movement and density of people to be controlled while providing ground truth information.…”

Section: Synthetic Composite Imagerymentioning

confidence: 99%

A Review of Synthetic Image Data and Its Use in Computer Vision

Man

Chahl

2022

J. Imaging

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Development of computer vision algorithms using convolutional neural networks and deep learning has necessitated ever greater amounts of annotated and labelled data to produce high performance models. Large, public data sets have been instrumental in pushing forward computer vision by providing the data necessary for training. However, many computer vision applications cannot rely on general image data provided in the available public datasets to train models, instead requiring labelled image data that is not readily available in the public domain on a large scale. At the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to the forefront as a potentially faster and cheaper alternative to collecting and annotating real data. This review provides general overview of types of synthetic image data, as categorised by synthesised output, common methods of synthesising different types of image data, existing applications and logical extensions, performance of synthetic image data in different applications and the associated difficulties in assessing data performance, and areas for further research.

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Section: Synthetic Composite Imagerymentioning

confidence: 99%

A Review of Synthetic Image Data and Its Use in Computer Vision

Man

Chahl

2022

J. Imaging

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“…A more direct approach to generating synthetic data has been developed by Ekbatani et al [168], who extract real pedestrians from images and add them at various locations on other backgrounds, with special improvement procedures for added realism; they also report improved counting results. Khadka et al [316] also present a synthetic crowd dataset, showing improvements in crowd counting.…”

Section: Synthetic Peoplementioning

confidence: 99%

Synthetic Data for Deep Learning

Nikolenko¹

2019

Preprint

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Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more); we also survey the work on improving synthetic data development and alternative ways to produce it such as GANs. Second, we discuss in detail the synthetic-to-real domain adaptation problem that inevitably arises in applications of synthetic data, including syntheticto-real refinement with GAN-based models and domain adaptation at the feature/model level without explicit data transformations. Third, we turn to privacy-related applications of synthetic data and review the work on generating synthetic datasets with differential privacy guarantees. We conclude by highlighting the most promising directions for further work in synthetic data studies.

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“…Consequently, simulation tools have been adopted for generating synthetic datasets to overcome the challenges associated with their real counterparts. Using simulation tools that can significantly reduce the time required to generate scenariospecific crowd datasets, mimic observed crowds in a realistic environment, facilitate data-driven research, and build functional machine learning models [10,11] based on generated data. Simulation offers flexibility in adjusting the scenarios, and generating and reproducing datasets with defined requirements.…”

Section: Introductionmentioning

confidence: 99%

Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

Foszner

Szczęsna

Ciampi

et al. 2023

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.

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Learning how to analyse crowd behaviour using synthetic data

Cited by 8 publications

References 15 publications

A Review of Synthetic Image Data and Its Use in Computer Vision

A Review of Synthetic Image Data and Its Use in Computer Vision

Synthetic Data for Deep Learning

Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods

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