Pose Variability Compensation Using Projective Transformation for Forensic Face Recognition

Gonzalez-Sosa, Ester; Vera-Rodríguez, Rubén; Fiérrez, Julián; Tomé, Pedro; Ortega‐Garcia, Javier

doi:10.1109/biosig.2015.7314615

Cited by 2 publications

(1 citation statement)

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“…A consistent coordinate system is required for all of the reported bounding boxes in order to find the spatial relationships between objects. For this paper, we use projective transformation to transform the image coordinate system into a geographic coordinate system [53][54][55][56]. Coordinate transformation allows the IoSC to project detected objects from different cameras onto a common coordinate system.…”

Section: Event Matching Using Cloud Computingmentioning

confidence: 99%

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Honarparvar

Saeedi

Liang

et al. 2021

IJGI

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Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models.

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Section: Event Matching Using Cloud Computingmentioning

confidence: 99%