Recently, the new Kinect One has been issued by Microsoft, providing the next
generation of real-time range sensing devices based on the Time-of-Flight (ToF)
principle. As the first Kinect version was using a structured light approach,
one would expect various differences in the characteristics of the range data
delivered by both devices. This paper presents a detailed and in-depth
comparison between both devices. In order to conduct the comparison, we propose
a framework of seven different experimental setups, which is a generic basis
for evaluating range cameras such as Kinect. The experiments have been designed
with the goal to capture individual effects of the Kinect devices as isolatedly
as possible and in a way, that they can also be adopted, in order to apply them
to any other range sensing device. The overall goal of this paper is to provide
a solid insight into the pros and cons of either device. Thus, scientists that
are interested in using Kinect range sensing cameras in their specific
application scenario can directly assess the expected, specific benefits and
potential problem of either device.

本文比较了微软的新 Kinect One 和之前使用结构光方法的 Kinect 版本的深度相机。通过七种不同的实验配置，旨在评估 Kinect 等范围摄像机，来捕捉设备的个体性能。从而为科学家在他们特定应用场景中使用 Kinect 范围感知相机提供解决方案。

Kinect 范围感知：结构光 VS 飞行时间式 Kinect

Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect

Driver inattention assessment has become a very active field in intelligent
transportation systems. Based on active sensor Kinect and computer vision
tools, we have built an efficient module for detecting driver distraction and
recognizing the type of distraction. Based on color and depth map data from the
Kinect, our system is composed of four sub-modules. We call them eye behavior
(detecting gaze and blinking), arm position (is the right arm up, down, right
of forward), head orientation, and facial expressions. Each module produces
relevant information for assessing driver inattention. They are merged together
later on using two different classification strategies: AdaBoost classifier and
Hidden Markov Model. Evaluation is done using a driving simulator and 8 drivers
of different gender, age and nationality for a total of more than 8 hours of
recording. Qualitative and quantitative results show strong and accurate
detection and recognition capacity (85% accuracy for the type of distraction
and 90% for distraction detection). Moreover, each module is obtained
independently and could be used for other types of inference, such as fatigue
detection, and could be implemented for real cars systems.

基于 Kinect 和计算机视觉技术，构建了一个有效的模块，用于检测驾驶员注意力分散和识别分散类型，每个子模块独立获得并可用于其他类型的推理，如疲劳检测，并可用于实际汽车系统。