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• W3110799135 abstract "Recent years witnessed a surge in the number of IoT cameras in smart cities. In this article, an ensemble learning-based prediction model for image forensics from IoT camera is proposed. In particular, our goal is to obtain human body measurements from 2D images taken from two views. Firstly, 24 body part features are extracted by the DensePose algorithm from the two views. Secondly, the features of the upper body part are integrated with height and body weight features. Ensemble learning is then performed with the LightGBM algorithm and a regression prediction model is constructed. The proposed noncontact image prediction method is simple and workable. Its feasibility and validity are verified on an experimental dataset. Experimental results demonstrate that the proposed method is highly reliable in the size prediction of different body parts. Specifically, the mean absolute errors of chest circumference, waistline and hip circumference are about 2.5 cm, while the mean absolute errors of other predictions are about 1 cm." @default.
• W3110799135 created "2020-12-21" @default.
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• W3110799135 date "2020-01-01" @default.
• W3110799135 modified "2023-10-17" @default.
• W3110799135 title "An Ensemble Learning-Based Prediction Model for Image Forensics From IoT Camera in Smart Cities" @default.
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• W3110799135 doi "https://doi.org/10.1109/access.2020.3043765" @default.
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