Abstract
In this work, we propose an architecture for predicting plausible person-object interactions based on image visible objects and room recognition. First, the system detects objects in the video using a popular framework named “YOLO” (You Only Look Once) and associates each object with their possible interactions. Then, making use of a convolutional neural network, our algorithm recognizes which is the room that appears in the image and filters possible context aware human-object interactions. The main purpose of this project is helping people with memory failures to perform daily activities. Many people have problems carrying out actions that can be natural for the rest. With the aim to assist them, we are interested in the development of methods which allow remembering them the actions they may have forgotten.
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Acknowledgement
This work has been funded by the Spanish Government PID2019-104818RB-I00 grant for the MoDeaAS project, supported with Feder funds. This work has also been supported by Spanish national grants for PhD studies FPU17/00166, ACIF/2018/197 and UAFPU2019-13. Experiments were made possible by a generous hardware donation from NVIDIA.
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Fernández, I.S.M., Oprea, S., Castro-Vargas, J.A., Martinez-Gonzalez, P., Garcia-Rodriguez, J. (2022). Estimating Context Aware Human-Object Interaction Using Deep Learning-Based Object Recognition Architectures. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_41
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