Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/231417
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dc.titleIMPROVING INDOOR OBJECT RECOGNITION USING 3D CONTEXT
dc.contributor.authorYE QIYUAN
dc.date.accessioned2022-09-27T18:00:22Z
dc.date.available2022-09-27T18:00:22Z
dc.date.issued2022-07-14
dc.identifier.citationYE QIYUAN (2022-07-14). IMPROVING INDOOR OBJECT RECOGNITION USING 3D CONTEXT. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/231417
dc.description.abstractThis dissertation focuses on the problem of indoor object recognition to help people with visual impairments (PVI). We envision a system where a PVI has a wearable device, equipped with a camera, that can identify the objects in a scene and transcribe it to audio to help the PVI in orientation and mobility. A naive approach toward indoor scene recognition is to use object recognition algorithms and label each object in the scene independently. In this dissertation, we wish to exploit 3D information of the indoor scene to improve the accuracy of object recognition. We assume that the 3D layout is published by venue owners (e.g., hotels, malls) to make the places more accessible to PVI. Using YOLOv4 as the core, we integrated information from the 3D layout of a scene into the indoor object recognition pipeline. Particularly, we include the distance between (fixed) objects in the scene, so that co-occurrence of objects contributes to the likelihood of an object being identified correctly. Using the AI2Thor indoor scene dataset, we showed that our method improve object recognition accuracy from 81% to 90%.
dc.language.isoen
dc.subjectIndoor Object Recognition, 3D Context, Adjacent Objects, Conditional Probability
dc.typeThesis
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.supervisorWei Tsang Ooi
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF SCIENCE (RSH-SOC)
Appears in Collections:Master's Theses (Open)

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