What will the future be? We wonder! In this survey, we explore the gap
between current research in egocentric vision and the ever-anticipated future,
where wearable computing, with outward facing cameras and digital overlays, is
expected to be integrated in our every day lives. To understand this gap, the
article starts by envisaging the future through character-based stories,
showcasing through examples the limitations of current technology. We then
provide a mapping between this future and previously defined research tasks.
For each task, we survey its seminal works, current state-of-the-art
methodologies and available datasets, then reflect on shortcomings that limit
its applicability to future research. Note that this survey focuses on software
models for egocentric vision, independent of any specific hardware. The paper
concludes with recommendations for areas of immediate explorations so as to
unlock our path to the future always-on, personalised and life-enhancing
egocentric vision.

对于可以整合到日常生活中的可穿戴计算机、外置摄像头和数字叠加技术等未来的先知性研究，本文通过以人物为基础的故事来描绘未来，并提供了之前定义的研究任务与未来之间的映射，对每个任务进行了调研，包括开创性的作品、最新技术方法和可用数据集，然后反思了目前技术的局限，最后提出了为了实现始终开启、个性化和增强式的自我中心视觉的未来研究的建议。

自我中心视觉的未来展望

An Outlook into the Future of Egocentric Vision

Human activity recognition (HAR) in wearable computing is typically based on
direct processing of sensor data. Sensor readings are translated into
representations, either derived through dedicated preprocessing, or integrated
into end-to-end learning. Independent of their origin, for the vast majority of
contemporary HAR, those representations are typically continuous in nature.
That has not always been the case. In the early days of HAR, discretization
approaches have been explored - primarily motivated by the desire to minimize
computational requirements, but also with a view on applications beyond mere
recognition, such as, activity discovery, fingerprinting, or large-scale
search. Those traditional discretization approaches, however, suffer from
substantial loss in precision and resolution in the resulting representations
with detrimental effects on downstream tasks. Times have changed and in this
paper we propose a return to discretized representations. We adopt and apply
recent advancements in Vector Quantization (VQ) to wearables applications,
which enables us to directly learn a mapping between short spans of sensor data
and a codebook of vectors, resulting in recognition performance that is
generally on par with their contemporary, continuous counterparts - sometimes
surpassing them. Therefore, this work presents a proof-of-concept for
demonstrating how effective discrete representations can be derived, enabling
applications beyond mere activity classification but also opening up the field
to advanced tools for the analysis of symbolic sequences, as they are known,
for example, from domains such as natural language processing. Based on an
extensive experimental evaluation on a suite of wearables-based benchmark HAR
tasks, we demonstrate the potential of our learned discretization scheme and
discuss how discretized sensor data analysis can lead to substantial changes in
HAR.

本文探讨了离散化方法在人类活动识别 (HAR) 领域的应用，作者采用了向量量化技术 (Vector Quantization，VQ)，提出了一种从短时间的传感器数据到码本向量的映射方法，证明了离散化方法不但可用于活动分类，还可用于符号序列分析。实验结果及讨论表明离散化分析传感器数据将会在 HAR 领域带来重大变革。