Improving the Background Awareness for Identifying Child Begging by Integrating Foreground and Background Dual Image Classifiers

Abstract

Image recognition technologies have found widespread application in domains such as object detection, medical imaging, and autonomous driving. Beyond these applications, they offer promising potential in tackling social challenges, such as identifying children involved in begging activities through intelligent analysis of visual data. The existing data on child begging is scarce and often presents outdated information. Our research demonstrates that image classification models, such as CNN, VGG16, and EfficientNet, can be effectively trained on images captured from public cameras to identify children engaged in begging. This data can be used for quicker and more effective interventions. To further improve detection, we integrated background learning into our approach. The classification models may struggle to distinguish between similar features across environments (for example, misidentifying a poorly dressed child in a ghetto as a beggar). Incorporating background learning can help mitigate such errors by providing contextual understanding. We further proposed an Integrated Dual Image Classifier to learn the background and foreground separately and then subsequently combine both model prediction probabilities. In this method, background understanding is incorporated with the foreground prediction for recognition. The test accuracy results from the integrated dual model approach showed a reduction in false negatives and false positives (Failure to detect actual instances and incorrect identification of false instances as accurate, respectively), with test accuracy above 70%.