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Object Instance Identification in Dynamic Environments

2022-06-10 18:38:10
Takuma Yagi, Md Tasnimul Hasan, Yoichi Sato
arXiv_CV
   
Abstract

We study the problem of identifying object instances in a dynamic environment where people interact with the objects. In such an environment, objects' appearance changes dynamically by interaction with other entities, occlusion by hands, background change, etc. This leads to a larger intra-instance variation of appearance than in static environments. To discover the challenges in this setting, we newly built a benchmark of more than 1,500 instances built on the EPIC-KITCHENS dataset which includes natural activities and conducted an extensive analysis of it. Experimental results suggest that (i) robustness against instance-specific appearance change (ii) integration of low-level (e.g., color, texture) and high-level (e.g., object category) features (iii) foreground feature selection on overlapping objects are required for further improvement.

Abstract (translated)

URL

https://arxiv.org/abs/2206.05319

PDF

https://arxiv.org/pdf/2206.05319.pdf

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