Recently, through development of several 3d vision systems, widely used in various applications, medical and biometric fields. Microsoft kinect sensor have been most of used camera among 3d vision systems. Microsoft kinect sensor can obtain depth images of a scene and 3d coordinates of human joints. Thus, anthropometric features can extractable easily. Anthropometric feature and 3d joint coordinate raw datas which captured from kinect sensor is unstable. The strongest reason for this, datas vary by distance between joints of individual and location of kinect sensor. Consequently, usage of this datas without kinect calibration and data optimization does not result in sufficient and healthy. In this study, proposed a novel method to calibrating kinect sensor and optimizing skeleton features. Results indicate that the proposed method is quite effective and worthy of further study in more general scenarios.

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative position of Kinect v2 sensors with a calibration wand and register the sensors' positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of the motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach, which discovers occluded joint data. The synchronization approach is based on an NTP protocol that synchronizes the time between the clocks of a server and clients dynamically, ensuring that the proposed system is a real-time system. Experiments for validating the proposed system are conducted from the perspective of calibration, occlusion, accuracy, and efficiency. Furthermore, to demonstrate the practical performance of our system, a comparison of previously developed motion capture systems (the linear trilateration approach and the geometric trilateration approach) with the benchmark OptiTrack system is conducted, therein showing that the accuracy of our proposed system is $38.3\%$ and 24.1% better than the two aforementioned trilateration systems, respectively.

Drones have been given'eyes' and a new algorithm to help them fly intelligently, reaching their target position when GPS is not available. Dr. Jiefei Wang, a researcher from UNSW Canberra Trusted Autonomy Group, used an Xbox Kinect sensor as an input camera to help drones'see' their environment. Jiefei developed algorithms to process the video footage image by image, to help the drones know their own speed, motion, and to detect obstacles so they can reach their target position--a completely autonomous system. "Depth information is crucial for locating objects," Jiefei says. "Human beings can use one eye to see the world but need two eyes to locate. For example, try closing one eye, then point your index fingers towards each other and bring them together. Most people will find this difficult."

Anyone concerned that Microsoft is evolving into a more accessible version of IBM, rather than the consumer company many would like it to be, isn't going to feel any better after the company's Build developer conference starting May 8 in Seattle. Two expected moves will reinforce that enterprise direction: a Kinect sensor for Azure, and two HoloLens apps that are being adapted for businesses using mixed reality. Microsoft chief executive Satya Nadella is expected to open Build on Monday by describing the "intelligent cloud and the intelligent edge," which has been Microsoft's unofficial mantra for about a year. Microsoft plans to define what it means by intelligent edge: By 2020, there will be about 30 billion connected devices, each generating about 1.5GB of data per day. Smart buildings and connected factories will add to that.

In July 2015, Google's public-relations machine was in full-on crisis mode. Earlier that year, the search giant announced Photos, an AI-driven app that used machine-learning to automatically tag and organize your pictures based on the people, places and things depicted in them. It was an exciting step forward, but Photos wasn't perfect. While the app was capable of recognizing some faces, it mistook others. It would have been easy to pass this off as a routine software bug if it weren't for the nature of the failure.

Microsoft's Kinect has already proved its credentials in reading simple hand and body movements in the gaming world. But now a team of Chinese researchers have added sign language to its motion-sensing capabilities. Scientists at Microsoft Research Asia recently demonstrated software that allows Kinect to read sign language using hand tracking. What's impressive is that it can do this in real-time, translating sign language to spoken language and vice versa at conversational speeds. The system, dubbed the Kinect Sign Language Translator, is capable of capturing a conversation from both sides.

Uniquely identifying individuals using anthropometric and gait data allows for passive biometric systems, where cooperation from the subjects being identified is not required. In this paper, we report on experiments using a novel data set composed of 140 individuals walking in front of a Microsoft Kinect sensor. We provide a methodology to extract anthropometric and gait features from this data and show results of applying different machine learning algorithms on subject identification tasks. Focusing on KNN classifiers, we discuss how accuracy varies in different settings, including number of individuals in a gallery, types of attributes used and number of considered neighbors. Finally, we compare the obtained results with other results in the literature, showing that our approach has comparable accuracy for large galleries.