Abstract
Visitor management and asset management are crucial in restricted places. This paper focuses on how artificial intelligence and image recognition can drive innovation in both visitor and asset management spaces by employing the latest advances in technology. The proposed solution, VAMDLE, is an Android application which uses deep transfer learning and Elasticsearch to facilitate the registration of visitors as well as the management of borrowed assets. TensorFlow was used to train a pre-trained model for assets image recognition and the new model was integrated into the Android application with the aid of the TFLite library. A restful web API was developed with the aid of Spring Boot to manage all the data used by the client application. The unique identifiers of the assets and of the employees were read and recognized using text recognition and regular expressions and Elasticsearch was used to automatically fill in forms. The use of these various tools and technologies resulted in an app with a simple interface, a very good classification accuracy and good average response time. The proposed system was able to register a classification accuracy of up to 97%.
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Seenundun, V., Purmah, B., Mungloo-Dilmohamud, Z. (2022). VAMDLE: Visitor and Asset Management Using Deep Learning and ElasticSearch. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_21
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