Abstract
Wearable Detection is a societally and economically critical yet a very challenging issue because of the number of layers and clothing someone could be wearing. Also layering, pose, body style, and shape become an issue. In this paper, we handle the wearable detection issue using recovery approaches. For model picture, we use the comparable styles from substantial database—labeled pictures and utilize cases to perceive dress things in the inquiry. Our tests come about moreover show that the general posture estimation issue can profit by apparel detection. In addition, for the correct detection and classification of what a person is wearing, we use the process of image segmentation and pose estimation to segment the image into superpixels and then analyze accordingly. In addition, we use a large novel dataset and tools for labeling garment items, to retrieve similar style to help with clothing classification.
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Jain, P., Kankani, A., Geraldine Bessie Amali, D. (2019). A New Technique for Accurate Segmentation, and Detection of Outfit Using Convolution Neural Networks. In: Satapathy, S., Bhateja, V., Somanah, R., Yang, XS., Senkerik, R. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 862. Springer, Singapore. https://doi.org/10.1007/978-981-13-3329-3_16
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DOI: https://doi.org/10.1007/978-981-13-3329-3_16
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