Publication

Abstract :

Child trafficking are increasing in urban locations and the count is only increasing. Mostly the children are abused and exploited for begging, involuntary domestic servitude, forced child labor, commercial sex, and other illegal activities. In India, trafficking has grown over 14 times over the last decade according to National Crimes Records Bureau (NCRB) and the country has become a transit point for traffickers. The necessity to prevent child trafficking and engaging them in self-development, is the responsibility of every citizen towards nation building. Technological advancements have taken a huge leap in the domain of mobile analytics and social networking and therefore it should be used as a tool to mitigate this burning issue. Empowering every individual to report child trafficking using mobile devices and let the technology do the rest of analytics to identify vulnerable spots, missing child, type of trafficking and reporting to authorities can make a difference. Therefore, our proposed work is to develop an mobile application environment with the general public as the frontline workers, who can capture an image or video of the scene of trafficking along with the location information. The crowd sourced data is pulled from the cloud and segregated based on location. Further the date is pre-processed using image processing algorithms specifically Convolutional Neural Network (CNN) is used. As a case study, the work identifies a missing child, by applying the crowdsourced data to the CNN based training algorithm to match with the available sources. The algorithm also classifies the gender and age using cascade classifier and physical impairments using MAX pooling filter. © 2021, Springer Nature Singapore Pte Ltd.