Systematic Review of Computer Vision Semantic Analysis in Socially Assistive Robotics

Abstract

The simultaneous surges in the research on socially assistive robotics and that on computer vision can be seen as a result of the shifting and increasing necessities of our global population, especially towards social care with the expanding population in need of socially assistive robotics. The merging of these fields creates demand for more complex and autonomous solutions, often struggling with the lack of contextual understanding of tasks that semantic analysis can provide and hardware limitations. Solving those issues can provide more comfortable and safer environments for the individuals in most need. This work aimed to understand the current scope of science in the merging fields of computer vision and semantic analysis in lightweight models for robotic assistance. Therefore, we present a systematic review of visual semantics works concerned with assistive robotics. Furthermore, we discuss the trends and possible research gaps in those fields. We detail our research protocol, present the state of the art and future trends, and answer five pertinent research questions. Out of 459 articles, 22 works matching the defined scope were selected, rated in 8 quality criteria relevant to our search, and discussed in depth. Our results point to an emerging field of research with challenging gaps to be explored by the academic community. Data on database study collection, year of publishing, and the discussion of methods and datasets are displayed. We observe that the current methods regarding visual semantic analysis show two main trends. At first, there is an abstraction of contextual data to enable an automated understanding of tasks. We also observed a clearer formalization of model compaction metrics.

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

“…Semantic segmentation plays a vital role in perceiving and interpreting images, crucial for appli-cations like autonomous driving and medical imaging. Convolutional neural networks, especially in deep learning, have significantly advanced semantic segmentation, providing high-resolution mapping for various applications, including YouTube stories and scene understanding [86][87][88][89][90][91][92]. This technique finds applications in diverse areas, such as document analysis, virtual makeup, self-driving cars, and background manipulation in images, showcasing its versatility and importance.…”

“…This technique finds applications in diverse areas, such as document analysis, virtual makeup, self-driving cars, and background manipulation in images, showcasing its versatility and importance. Semantic segmentation architectures typically involve an encoder network, which utilizes pre-trained networks like VGG or ResNet, and a decoder network, which projects learned features onto the pixel space, enabling dense pixel-level classification [86][87][88][89][90][91][92].…”

“…[12,[81][82][83][84][85] Semantic segmentation assigns labels to every pixel, significantly enhanced by deep learning, particularly CNNs. [86][87][88][89][90][91][92] Describe components of a semantic segmentation architecture and three main approaches: region-based, FCN-based, and weakly supervised. [50,63,86,87,[90][91][92][97][98][99][100][101][102][103][104][105][106][107] Semantic segmentation, focusing on medical image analysis and DCNNs.…”