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.
Highlights
In recent years, IoT cameras have become ubiquitous in smart cities, and these sensors are widely adopted for forensic applications [1], [2]
We propose an ensemble learning-based prediction model that allows forensic evidence to be extracted from images both efficiently and effectively
Noncontact human body size measurement of different body parts based on the static images which are taken by cameras can improve user experiences, and can save material and financial costs
Summary
IoT cameras have become ubiquitous in smart cities, and these sensors are widely adopted for forensic applications [1], [2]. Noncontact human body size measurement of different body parts based on the static images which are taken by cameras can improve user experiences, and can save material and financial costs. Virtual fit products on the market include Fitiquette (USA), Fits.me (UK) and Pulshion (China) All of these products generally require users to fill in relevant body sizes firstly, generate the corresponding 3D anthropometric dummy, and render the dressing effect onto the 3D anthropometric dummy [8]– [10]. For these products, precision of body size data which is input by users can affect the reliability of online fitting
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
