Browsing by Subject "Kinect"
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Item Human detection and action recognition using depth information by Kinect(2012-05) Xia, Lu, active 21st century; Grauman, Kristen Lorraine, 1979-Traditional computer vision algorithms depend on information taken by visible-light cameras. But there are inherent limitations of this data source, e.g. they are sensitive to illumination changes, occlusions and background clutter. Range sensors give us 3D structural information of the scene and it’s robust to the change of color and illumination. In this thesis, we present a series of approaches which are developed using the depth information by Kinect to address the issues regarding human detection and action recognition. Taking the depth information, the basic problem we consider is to detect humans in the scene. We propose a model based approach, which is comprised of a 2D head contour detector and a 3D head surface detector. We propose a segmentation scheme to segment the human from the surroundings based on the detection point and extract the whole body of the subject. We also explore the tracking algorithm based on our detection result. The methods are tested on a dataset we collected and present superior results over the existing algorithms. With the detection result, we further studied on recognizing their actions. We present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.’s method. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action3D dataset and our algorithm outperforms existing algorithm on most of the cases.Item Realization Of A Spatial Augmented Reality System - A Digital Whiteboard Using a Kinect Sensor and a PC Projector(2013-05-01) Kolomenski, Andrei ARecent rapid development of cost-effective, accurate digital imaging sensors, high-speed computational hardware, and tractable design software has given rise to the growing field of augmented reality in the computer vision realm. The system design of a 'Digital Whiteboard' system is presented with the intention of realizing a practical, cost-effective and publicly available spatial augmented reality system. A Microsoft Kinect sensor and a PC projector coupled with a desktop computer form a type of spatial augmented reality system that creates a projection based graphical user interface that can turn any wall or planar surface into a 'Digital Whiteboard'. The system supports two kinds of user inputs consisting of depth and infra-red information. An infra-red collimated light source, like that of a laser pointer pen, serves as a stylus for user input. The user can point and shine the infra-red stylus on the selected planar region and the reflection of the infra-red light source is registered by the system using the infra-red camera of the Kinect. Using the geometric transformation between the Kinect and the projector, obtained with system calibration, the projector displays contours corresponding to the movement of the stylus on the 'Digital Whiteboard' region, according to a smooth curve fitting algorithm. The described projector-based spatial augmented reality system provides new unique possibilities for user interaction with digital content.Item Security with visual understanding : Kinect human recognition capabilities applied in a home security system(2012-05) Fluckiger, S Joseph; Garg, Vijay K. (Vijay Kumar), 1963-; Bard, WilliamVision is the most celebrated human sense. Eighty percent of the information humans receive is obtained through vision. Machines capable of capturing images are now ubiquitous, but until recently, they have been unable to recognize objects in the images they capture. In effect, machines have been blind. This paper explores the revolutionary new capability of a camera to recognize whether a human is present in an image and take detailed measurements of the person’s dimensions. It explains how the hardware and software of the camera work to provide this remarkable capability in just 200 milliseconds per image. To demonstrate these capabilities, a home security application has been built called Security with Visual Understanding (SVU). SVU is a hardware/software solution that detects a human and then performs biometric authentication by comparing the dimensions of the seen person against a database of known people. If the person is unrecognized, an alarm is sounded, and a picture of the intruder is sent via SMS text message to the home owner. Analysis is performed to measure the tolerance of the SVU algorithm for differentiating between two people based on their body dimensions.