Resumen
The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.