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TopView: vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning.

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Title: TopView: vectorising road users in a bird's eye view from uncalibrated street-level imagery with deep learning.
Authors: Ibrahim, Mohamed R.1 (AUTHOR) m.ibrahim1@leeds.ac.uk
Source: Neural Computing & Applications. Jun2025, Vol. 37 Issue 18, p11991-12011. 21p.
Subjects: Road users, Computer vision, Artificial intelligence, Orthographic projection, Image processing
Abstract: Generating a bird's eye view of road users is beneficial for a variety of applications, including navigation, detecting agent conflicts, and measuring space occupancy, as well as the ability to utilise the metric system to measure distances between different objects. In this research, we introduce a simple approach for estimating a bird's eye view from images without prior knowledge of a given camera's intrinsic and extrinsic parameters. The model is based on the orthogonal projection of objects from various fields of view to a bird's eye view by learning the vanishing point of a given scene. Additionally, we utilised the learned vanishing point alongside the trajectory line to transform the 2D bounding boxes of road users into 3D bounding information. The introduced framework has been applied to several applications to generate a live Map from camera feeds and to analyse social distancing violations at the city scale. The introduced framework shows a high validation in geolocating road users in various uncalibrated cameras. It also paves the way for new adaptations in urban modelling techniques and simulating the built environment accurately, which could benefit agent-based modelling by relying on deep learning and computer vision. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Generating a bird's eye view of road users is beneficial for a variety of applications, including navigation, detecting agent conflicts, and measuring space occupancy, as well as the ability to utilise the metric system to measure distances between different objects. In this research, we introduce a simple approach for estimating a bird's eye view from images without prior knowledge of a given camera's intrinsic and extrinsic parameters. The model is based on the orthogonal projection of objects from various fields of view to a bird's eye view by learning the vanishing point of a given scene. Additionally, we utilised the learned vanishing point alongside the trajectory line to transform the 2D bounding boxes of road users into 3D bounding information. The introduced framework has been applied to several applications to generate a live Map from camera feeds and to analyse social distancing violations at the city scale. The introduced framework shows a high validation in geolocating road users in various uncalibrated cameras. It also paves the way for new adaptations in urban modelling techniques and simulating the built environment accurately, which could benefit agent-based modelling by relying on deep learning and computer vision. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Jun2025
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