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Recommendation systems based on image recognition could prove a vital tool inenhancing the experience of museum audiences. However, for practical systemsutilizing wearable cameras, a number of challenges exist which affect thequality of image recognition. In this pilot study, we focus on recognition ofmuseum collections by using a wearable camera in three different museum spaces.We discuss the application of wearable cameras, and the practical and technicalchallenges in devising a robust system that can recognize artworks viewed bythe visitors to create a detailed record of their visit. Specifically, toillustrate the impact of different kinds of museum spaces on image recognition,we collect three training datasets of museum exhibits containing variety ofpaintings, clocks, and sculptures. Subsequently, we equip selected visitorswith wearable cameras to capture artworks viewed by them as they stroll alongexhibitions. We use Convolutional Neural Networks (CNN) which are pre-trainedon the ImageNet dataset and fine-tuned on each of the training sets for thepurpose of artwork identification. In the testing stage, we use CNNs toidentify artworks captured by the visitors with a wearable camera. We analyzethe accuracy of their recognition and provide an insight into the applicabilityof such a system to further engage audiences with museum exhibitions.

Artwork Identification from Wearable Camera Images for Enhancing Experience of Museum Audiences