{"title":"未来的手指和它的现代应用","authors":"Tanmay Sankhe, Pranav Puranik, Masira Mulla","doi":"10.1109/ICICT46931.2019.8977629","DOIUrl":null,"url":null,"abstract":"Gesture regulated systems controlled by smart wearables have spearheaded the next epoch in human machine interaction. Gestures being intuitive and expressive are a more convenient way of communication. However, for developing a gesture controlled system, we need to accurately detect fingertips. In this paper, we present a fingertip detection system that can be efficiently used by smart wearables. This approach is free of markers and centroid-based techniques which are traditionally used to detect fingertips.The system’s functionality is controlled by the number of fingertips in the frame. We collated a customized dataset, ‘1-2-3-4 Hands’, which contained the images of different hands gesturing using one to four fingers. Using Faster RCNN with Inception v2 module, we trained this dataset to build a model capable of recognizing any of the first four fingertips, excluding the thumb. The count of fingertips is used to perform an action in real-time gesture controlled systems. Finally, we have implemented finger-control solutions such as AirWriting, AirDrawing, and Gaming Controls and enlisted their benefits.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Futuristic Finger and its Modern Day Applications\",\"authors\":\"Tanmay Sankhe, Pranav Puranik, Masira Mulla\",\"doi\":\"10.1109/ICICT46931.2019.8977629\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gesture regulated systems controlled by smart wearables have spearheaded the next epoch in human machine interaction. Gestures being intuitive and expressive are a more convenient way of communication. However, for developing a gesture controlled system, we need to accurately detect fingertips. In this paper, we present a fingertip detection system that can be efficiently used by smart wearables. This approach is free of markers and centroid-based techniques which are traditionally used to detect fingertips.The system’s functionality is controlled by the number of fingertips in the frame. We collated a customized dataset, ‘1-2-3-4 Hands’, which contained the images of different hands gesturing using one to four fingers. Using Faster RCNN with Inception v2 module, we trained this dataset to build a model capable of recognizing any of the first four fingertips, excluding the thumb. The count of fingertips is used to perform an action in real-time gesture controlled systems. Finally, we have implemented finger-control solutions such as AirWriting, AirDrawing, and Gaming Controls and enlisted their benefits.\",\"PeriodicalId\":412668,\"journal\":{\"name\":\"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICT46931.2019.8977629\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICT46931.2019.8977629","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gesture regulated systems controlled by smart wearables have spearheaded the next epoch in human machine interaction. Gestures being intuitive and expressive are a more convenient way of communication. However, for developing a gesture controlled system, we need to accurately detect fingertips. In this paper, we present a fingertip detection system that can be efficiently used by smart wearables. This approach is free of markers and centroid-based techniques which are traditionally used to detect fingertips.The system’s functionality is controlled by the number of fingertips in the frame. We collated a customized dataset, ‘1-2-3-4 Hands’, which contained the images of different hands gesturing using one to four fingers. Using Faster RCNN with Inception v2 module, we trained this dataset to build a model capable of recognizing any of the first four fingertips, excluding the thumb. The count of fingertips is used to perform an action in real-time gesture controlled systems. Finally, we have implemented finger-control solutions such as AirWriting, AirDrawing, and Gaming Controls and enlisted their benefits.