This paper proposes a system for automatic social pattern characterization
using a wearable photo-camera. The proposed pipeline consists of three major
steps. First, detection of people with whom the camera wearer interacts and,
second, categorization of the detected social interactions into formal and
informal. These two steps act at event-level where each potential social event
is modeled as a multi-dimensional time-series, whose dimensions correspond to a
set of relevant features for each task, and a LSTM network is employed for
time-series classification. In the last step, recurrences of the same person
across the whole set of social interactions are clustered to achieve a
comprehensive understanding of the diversity and frequency of the social
relations of the user. Experiments over a dataset acquired by a user wearing a
photo-camera during a month show promising results on the task of social
pattern characterization from egocentric photo-streams.

该论文提出了一种使用可穿戴照相机实现自动社交模式刻画的系统，其中通过检测与相机佩戴者互动的人，并将检测到的社交互动分类为正式和非正式两个步骤，在事件级别上对每个潜在的社交事件进行建模，并使用 LSTM 网络进行时间序列分类。最终通过聚类同一个人在整个社交互动集中的出现来全面理解用户的社交关系的多样性和频率。实验表明，该系统应用于自我中心的照片流上的社交模式刻画任务取得了有望的结果。

基于自我中心照片流的社交风格特征刻画

Social Style Characterization from Egocentric Photo-streams

Activity recognition from long unstructured egocentric photo-streams has
several applications in assistive technology such as health monitoring and
frailty detection, just to name a few. However, one of its main technical
challenges is to deal with the low frame rate of wearable photo-cameras, which
causes abrupt appearance changes between consecutive frames. In consequence,
important discriminatory low-level features from motion such as optical flow
cannot be estimated. In this paper, we present a batch-driven approach for
training a deep learning architecture that strongly rely on Long short-term
units to tackle this problem. We propose two different implementations of the
same approach that process a photo-stream sequence using batches of fixed size
with the goal of capturing the temporal evolution of high-level features. The
main difference between these implementations is that one explicitly models
consecutive batches by overlapping them. Experimental results over a public
dataset acquired by three users demonstrate the validity of the proposed
architectures to exploit the temporal evolution of convolutional features over
time without relying on event boundaries.

本文研究利用深度学习，以图像流中的高级特征为基础识别佩戴式摄像机的运动，并弥补设备低帧率带来的连续帧之间的差异罅隙，从而提升了辅助技术应用中的活动识别效能。