Aerial cinematography is revolutionizing industries that require live and
dynamic camera viewpoints such as entertainment, sports, and security. However,
safely piloting a drone while filming a moving target in the presence of
obstacles is immensely taxing, often requiring multiple expert human operators.
Hence, there is demand for an autonomous cinematographer that can reason about
both geometry and scene context in real-time. Existing approaches do not
address all aspects of this problem; they either require high-precision
motion-capture systems or GPS tags to localize targets, rely on prior maps of
the environment, plan for short time horizons, or only follow artistic
guidelines specified before flight.
In this work, we address the problem in its entirety and propose a complete
system for real-time aerial cinematography that for the first time combines:
(1) vision-based target estimation; (2) 3D signed-distance mapping for
occlusion estimation; (3) efficient trajectory optimization for long
time-horizon camera motion; and (4) learning-based artistic shot selection. We
extensively evaluate our system both in simulation and in field experiments by
filming dynamic targets moving through unstructured environments. Our results
indicate that our system can operate reliably in the real world without
restrictive assumptions. We also provide in-depth analysis and discussions for
each module, with the hope that our design tradeoffs can generalize to other
related applications. Videos of the complete system can be found at:
this https URL

本研究提出了一个完整的系统，通过结合基于视觉的目标估计、三维有符号距离映射、高效的轨迹优化以及基于学习的艺术镜头选择，实现了实时的无人机拍摄，成功解决了多种限制性的问题。

基于学习的艺术决策的无人机在非结构化环境下的航拍摄影

Autonomous Aerial Cinematography In Unstructured Environments With  Learned Artistic Decision-Making

Sounds are an important source of information on our daily interactions with
objects. For instance, a significant amount of people can discern the
temperature of water that it is being poured just by using the sense of
hearing. However, only a few works have explored the use of audio for the
classification of object interactions in conjunction with vision or as single
modality. In this preliminary work, we propose an audio model for egocentric
action recognition and explore its usefulness on the parts of the problem
(noun, verb, and action classification). Our model achieves a competitive
result in terms of verb classification (34.26% accuracy) on a standard
benchmark with respect to vision-based state of the art systems, using a
comparatively lighter architecture.

本研究提出了一种音频模型，用于主体中心的动作识别，通过轻量化的架构，在视觉基础的标准数据集上取得了有竞争力的动词分类结果（34.26% 准确率）

识别自我为中心物体交互，音频有多重要？

How Much Does Audio Matter to Recognize Egocentric Object Interactions?

Vision-based automatic counting of people has widespread applications in
intelligent transportation systems, security, and logistics. However, there is
currently no large-scale public dataset for benchmarking approaches on this
problem. This work fills this gap by introducing the first real-world RGB-D
People Counting DataSet (PCDS) containing over 4,500 videos recorded at the
entrance doors of buses in normal and cluttered conditions. It also proposes an
efficient method for counting people in real-world cluttered scenes related to
public transportations using depth videos. The proposed method computes a point
cloud from the depth video frame and re-projects it onto the ground plane to
normalize the depth information. The resulting depth image is analyzed for
identifying potential human heads. The human head proposals are meticulously
refined using a 3D human model. The proposals in each frame of the continuous
video stream are tracked to trace their trajectories. The trajectories are
again refined to ascertain reliable counting. People are eventually counted by
accumulating the head trajectories leaving the scene. To enable effective head
and trajectory identification, we also propose two different compound features.
A thorough evaluation on PCDS demonstrates that our technique is able to count
people in cluttered scenes with high accuracy at 45 fps on a 1.7 GHz processor,
and hence it can be deployed for effective real-time people counting for
intelligent transportation systems.

本文提出了用于智能交通系统中真实世界杂乱场景下人数自动计数的方法，其基于 RGB-D 数据集，使用深度视频的点云计算方法、3D 人体模型和轨迹跟踪来计数，得到高精度的计数结果。