[ KINECTONE] HowTo: Detect Hands State, Like Open Or Closed hand
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A very interesting algorithm for shape/object recognition in general is called implicit shape model. In order to detect a global object (such as a car, or an open hand), the idea is first to detect possible parts of it (e.g. wheels, trunk, etc, or fingers, palm, wrist etc) using a local feature detector, and then to infer the position of the global object by considering the density and the relative position of its parts. For instance, if I can detect five fingers, a palm and a wrist in a given neighborhood, there's a good chance that I am in fact looking at a hand, however, if I only detect one finger and a wrist somewhere, it could be a pair of false detections. The academic research article on this implicit shape model algorithm can be found here.
If you only need the detection of a fist/grab state, you should give microsoft a chance. Microsoft.Kinect.Toolkit.Interaction contains methods and events that detects the grip / grip release state of a hand. Take a look at the HandEventType of InteractionHandPointer . That works quite good for the fist/grab detection, but does not detect or report the position of individual fingers.
The next kinect (kinect one) detects 3 joint per hand (Wrist, Hand, Thumb) and has 3 hand based gestures: open, closed (grip/fist) and lasso (pointer). If that is enough for you, you should consider the microsoft libraries.
Once Kinect has a pixel-by-pixel depth image, Kinect uses a type of edge detection here to delineate closer objects from the background of the shot, incorporating input from the regular visible light camera. The unit then attempts to track any moving objects from this, with the assumption that only people will be moving around in the image, and isolates the human shapes from the image. The unit's software, aided by artificial intelligence, performs segmentation of the shapes to try to identify specific body parts, like the head, arms, and hands, and track those segments individually. Those segments are used to construct a 20-point skeleton of the human body, which then can be used by game or other software to determine what actions the person has performed.
One of the simplest examples of human ECE is surface-constrained grasp from the top. Consider, as example, when we want to grasp an object from a table. We tend to cage the object within the hand and then slide the fingers on the table surface to establish contact with the object. A general strategy for top grasps with soft hands has been presented by Pozzi et al. (2018), where a functional model of the closure motion of a robotic hand is used to properly align soft robotic hands with the object to be grasped. However, there are several objects that cannot be grasped from the top when lying on a hard surface, e.g., flat or small objects. In these cases, other strategies are needed to robustly grasp them. Observing how humans grasp these types of objects indicates that we typically grasp them with a flip or a slide-to-edge grasp (Puhlmann et al., 2016). Both these strategies require rather complex motions, and there are still few works addressing the problem of performing them with robots. Flip-and-pinch grasps were achieved with an open-loop control strategy using an underactuated gripper in Odhner et al. (2013). In Babin and Gosselin (2018) and Salvietti et al. (2019), instead, flat objects were picked up by using dedicated tools that, similarly to a scoop, can slide under the object and lift it.
The strategy that is considered in this paper is the so-called slide-to-edge grasp, where the object is dragged toward the table limit through sliding and is then grasped from the edge (Eppner et al., 2015; Heinemann et al., 2015). Implementing it with robot hands poses several challenges. Eppner et al. (2015) devised two possible strategies, depending on the used hand. The Barrett Hand, that is rigid, first cages the object and then moves it toward the edge. The pneumatic RBO Hand 1 is instead placed so to have the palm pressing against the edge and the fingers free to interact with the object and drag it toward the palm. Sarantopoulos and Doulgeri (2018) considered different strategies depending on whether object was laying on the edge of a shelf (or a table), with void space just under it, or of a closed obstructing cupboard, without empty space beyond the edge. Hang et al. (2019) used one of the two fingers of a compliant gripper to stick to the object and drag it toward the edge. The motion was planned using an extended Constrained Bi-directional Rapidly-Exploring Random Tree (CBiRRT). Then, the protruding part of the object was grasped with a separate robot action (regrasp).
I like this Drawing tool and would like to implement in my project. But my project does not need to track the body. It is just tracking the Multiple hands. So is it possible to integrate it in that kind of project.
ohh ok..Actually i am not tracking the body instead i just tracked the multiple hands using EMGUCV also my kinect is put downwards to the TABLE surface so it cant see or detect the body. Now i would like to make a drawing tool. ?
The Minority Report has been in rotation on cable lately, andyou've probably seen the futuristic vision of Tom Cruise standing infront of a large screen, manipulating information with waves of his hands.That vision is a bit closer to reality, thanks in part to the economiesof scale of the game industry. I don't often have reason to sing thepraises of Microsoft, particularly not in a magazine devoted to Linux andall things open. But one thing our friends in Redmond do very well is tocommoditize hardware. They've done just that with the Kinect by creatingit as a natural interface for the Xbox 360 game console. What's more,they've allowed open-source developers to create drivers for the device,and they've even allowed the third party who developed the technology, PrimeSense,Inc., to release its own device drivers for Linux, Windows and OS X.
In this section we look at how some of these metrics should be measured, with main emphasis on how this can be extended to a production line for pass-fail criteria. However, it is very important to note that this is a subset of all the different metrics that should be evaluated to understand the full performance of any depth camera technology, and care should be taken to not highlight one simple test, like the indoor flat-white-wall test, over more realistic depth scenes. For example, a camera can be tuned to provide excellent results on a flat-wall by strongly leveraging hole-filling, temporal averaging, and spatial averaging depth filters, but the tradeoff is likely to be that non-flat real shapes (ex: spheres, heads) end up becoming quite distorted and flattened. While this type of complete technology evaluation will not be described below, it is recommended that usage specific tests should be incorporated in a test flow and may include using a mannequin head or various geometric shapes and materials and colors (balls, rubber hands, sticks, carpets) at varying distances, for example.
Few weeks ago I received my Kinect for Windows version 2 and private SDK so I finally got to try it out, the new Kinect ships with many improvements from v1 such as an Full HD Camera, thumb and hand open/close detection, better microphone, improved infrared and several applications can use the sensor at the same time. 2b1af7f3a8