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Understanding the degree of satisfaction for visitors has been a key factor in selecting attractive collections and designing appealing layouts in art galleries and museums. Although monitoring the actual spatiotemporal behaviors of visitors is essential for this purpose, introducing an expensive monitoring system would impose a heavy burden on the financial management and leads to unwanted restrictions on the layout design in the exhibition rooms. This paper presents an approach to visualizing the spatiotemporal changes in the maps of visitors' interest with a system of installed single-board computers such as Raspberry Pi devices. Employing single-board computers as IoT sensors facilitates monitoring systems to maximally covers the entire exhibition space while keeping the associated installation cost and power consumption sufficiently low. Our approach for this novel system organization begins by first detecting individuals from camera images using machine learning techniques and reconstructing their spatial positions from perspective views. Kernel density estimation was employed to represent the distribution of interest across the entire exhibition room as a continuous function by respecting the reconstructed positions of visitors. This allowed the use of heatmaps to visualize the changes in the map of interest reflecting the travel history of individual visitors and the accumulated distribution of interest over a specific period. Experimental results from eight months of measurement data demonstrate the capability of the proposed approach, including meaningful trends that reveal how the layout of collections attracted visitors to the exhibitions.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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