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Gesture recognition is the process of understanding and interpreting meaningful movements of the hands, arms, face, or sometimes head. It is of great need in designing an efficient human-computer interface. The technology has been in study in recent years because of its potential for application in user interfaces.Gesture recognition is one of the main areas of research for the engineers and scientists. It is the natural way of human machine interaction. Today the industry is working on different application to make interactions more easy, natural and convenient without wearing any extra device. There are many key top people in gesture recognition systems who have marketed their solution for various issues. The key vendors in gesture recognition space include Cognitec-Systems, eyeSight Mobile Ltd.,GestureTek Technologies Inc., Omek Interactive Ltd., PointGrab Ltd., PrimeSense Ltd., and SoftKinetic Inc. Some more to name are Intel Corp., Qualcomm Inc., and Thalmic Labs Inc...
In this paper we present what gesture recognition technology is all about and conceptualizing an ideal approach for gesture recognition.
S.N TITLE PAGE
1 Introduction...................................................... 1
2 Gesture types................................................... 2
3 Input devices.................................................... 2
4 Algorithms........................................................ 5-9
o D model-based algorithms
o Skeletal-based algorithms
o Appearance-based models
5 Architecture..................................................... 10
6 Application Development................................. 11
5 Challenges....................................................... 12-13
o "Gorilla arm"
7 Market Trend.................................................... 13
8 Factors driving the need of solution................. 13
9 Upcoming New Technology............................. 15
10 Construction..................................................... 16
11 Advantage and Disadvantage.......................... 17
11 Conclusion........................................................ 18
12 References........................................................ 19
It is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion recognition from face and hand gesture recognition. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques. Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices. Using the concept of gesture recognition, it is possible to point a finger at the computer screen so that the cursor will move accordingly. This could potentially make conventional input devices such as mouse, keyboards and even touch-screens redundant.Gesture recognition can be conducted with techniques from computer vision and image processing.
The literature includes ongoing work in the computer vision field on capturing gestures or more general human pose and movements by cameras connected to a computer.
Gesture recognition and pen computing:
Pen computing reduces the hardware impact of a system and also increases the range of physical world objects usable for control beyond traditional digital objects like keyboards and mice. Such implements can enable a new range of hardware not requiring monitors. This idea may lead to the creation of holographic display. The term gesture recognition has been used to refer more narrowly to non-text-input handwriting symbols, such as inking on a graphics tablet, multi-touch gestures, and mouse gesture recognition. This is computer interaction through the drawing of symbols with a pointing device cursor.
In computer interfaces, two types of gestures are distinguished. We consider online gestures, which can also be regarded as direct manipulations like scaling and rotating. In contrast, offline gestures are usually processed after the interaction is finished; e. g. a circle is drawn to activate a context menu.
Offline gestures: Those gestures that are processed after the user interaction with the object. An example is the gesture to activate a menu.
Online gestures: Direct manipulation gestures. They are used to scale or rotate a tangible object.
The ability to track a person's movements and determine what gestures they may be performing can be achieved through various tools. Although there is a large amount of research done in image/video based gesture recognition,
there is some variation within the tools and environments used between implementations.
Wired gloves. These can provide input to the computer about the position and rotation of the hands using magnetic or inertial tracking devices. Furthermore, some gloves can detect finger bending with a high degree of accuracy (5-10 degrees), or even provide haptic feedback to the user, which is a simulation of the sense of touch. The first commercially available hand-tracking glove-type device was the DataGlove, a glove-type device which could detect hand position, movement and finger bending. This uses fiber optic cables running down the back of the hand. Light pulses are created and when the fingers are bent, light leaks through small cracks and the loss is registered, giving an approximation of the hand pose.
Depth-aware cameras. Using specialized cameras such as structured light or time-of-flight cameras, one can generate a depth map of what is being seen through the camera at a short range, and use this data to approximate a 3D representation of what is being seen. These can be effective for detection of hand gestures due to their short range capabilities.
Stereo cameras. Using two cameras whose relations to one another are known, a 3d representation can be approximated by the output of the cameras. To get the cameras' relations, one can use a positioning reference such as a lexian-stripe or infrared emitters. In combination with direct motion measurement (6D-Vision) gestures can directly be detected.
Controller-based gestures. These controllers act as an extension of the body so that when gestures are performed, some of their motion can be conveniently captured by software. Mouse gestures are one such
example, where the motion of the mouse is correlated to a symbol being drawn by a person's hand, as is the Wii Remote or the Myo, which can study changes in acceleration over time to represent gestures. Devices such as the LG Electronics Magic Wand, the Loop and the Scoop use Hillcrest Labs' Freespace technology, which uses MEMS accelerometers, gyroscopes and other sensors to translate gestures into cursor movement. The software also compensates for human tremor and inadvertent movement. AudioCubes are another example. The sensors of these smart light emitting cubes can be used to sense hands and fingers as well as other objects nearby, and can be used to process data. Most applications are in music and sound synthesis, but can be applied to other fields.
Single camera. A standard 2D camera can be used for gesture recognition where the resources/environment would not be convenient for other forms of image-based recognition. Earlier it was thought that single camera may not be as effective as stereo or depth aware cameras, but some companies are challenging this theory. Software-based gesture recognition technology using a standard 2D camera that can detect robust hand gestures, hand signs, as well as track hands or fingertip at high accuracy has already been embedded in Lenovo’s Yoga ultrabooks, Pantech’s Vega LTE smartphones, Hisense’s Smart TV models, among other devices.
Different ways of tracking and analyzing gestures exist, and some basic layout is given is in the diagram above. For example, volumetric models convey the necessary information required for an elaborate analysis, however they prove to be very intensive in terms of computational power and require further technological developments in order to be implemented for real-time analysis. On the other hand, appearance-based models are easier to process but usually lack the generality required for Human-Computer Interaction.
Depending on the type of the input data, the approach for interpreting a gesture could be done in different ways. However, most of the techniques rely on key pointers represented in a 3D coordinate system. Based on the relative motion of these, the gesture can be detected with a high accuracy, depending of the quality of the input and the algorithm’s approach. In order to interpret movements of the body, one has to classify them according to common properties and the message the movements may express. For example, in sign language each gesture represents a word or phrase. The taxonomy that seems very appropriate for Human-Computer Interaction has been proposed by
Quek in "Toward a Vision-Based Hand Gesture Interface".He presents several interactive gesture systems in order to capture the whole space of the gestures:
Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based. The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. On the other hand, Appearance-based systems use images or videos for direct interpretation.
A real hand (left) is interpreted as a collection of vertices and lines in the 3D mesh version (right), and the software uses their relative position and interaction in order to infer the gesture.
3D model-based algorithms:The 3D model approach can use volumetric or skeletal models, or even a combination of the two. Volumetric approaches have been heavily used in computer animation industry and for computer vision purposes. The models are generally created of complicated 3D surfaces, like NURBS or polygon meshes. The drawback of this method is that is very computational intensive, and systems for live analysis are still to be developed. For the moment, a more interesting approach would be to map simple primitive objects to the person’s most important body parts ( for example cylinders for the arms and neck, sphere for the head) and analyse the way these interact with each other. Furthermore, some abstract structures like super-quadrics and generalised cylinders may be even more suitable for approximating the body parts. The exciting thing about this approach is that the parameters for these objects are quite simple. In order to better model the relation between these, we make use of constraints and hierarchies between our objects.
The skeletal version (right) is effectively modelling the hand (left). This has fewer parameters than the volumetric version and it's easier to compute, making it suitable for real-time gesture analysis systems.
Skeletal-based algorithms:Instead of using intensive
processing of the 3D models and dealing with a lot of
parameters, one can just use a simplified version of joint angle parameters along with segment lengths. This is known as a skeletal representation of the body, where a virtual skeleton of the person is computed and parts of the body are mapped to certain segments. The analysis here is done using the position and orientation of these segments and the relation between each one of them( for example the angle between the joints and the relative position or orientation)
Advantages of using skeletal models:
Algorithms are faster because only key parameters are analyzed.
Pattern matching against a template database is possible
Using key points allows the detection program to focus on the significant parts of the body
These binary silhouette(left) or contour(right) images represent typical input for appearance-based algorithms. They are compared with different hand templates and if they match, the correspondent gesture is inferred.
These models don’t use a spatial representation of the body anymore, because they derive the parameters directly from the images or videos using a template database. Some are based on the deformable 2D templates of the human parts of the body, particularly hands. Deformable templates are sets of points on the outline of an object, used as interpolation nodes for the object’s outline approximation. One of the simplest interpolation function is linear, which performs an average shape from point sets, point variability parameters and external deformators. These template-based models are mostly used for hand-tracking, but could also be of use for simple gesture classification.
A second approach in gesture detecting using appearance-based models uses image sequences as gesture templates. Parameters for this method are either the images themselves, or certain features derived from these. Most of the time, only one ( monoscopic) or two ( stereoscopic ) views are used.
The solution should act as middleware to form glue between platform and application. A sensor collect and sends raw data to the solution. The solution is a middleware that sits between the platform and application. It receives raw data from the sensor,processes and send high level data to application various vision based gesture application for market such as TV, Automobile, healthcare and many others can be built on top of the solution.
The solution effectiveness is judged by the solution usage. The aim of the solution will be to allow developers to easily create a variety of natural gesture control applications such as games, consumer electronics, and car infotainment solution.
Let us consider the application example of car infotainment solution to illustrate how effectively this ideal solution will be used. Automobile manufacturers are benefitted with gesture recognition as the technology is adding more value to their offerings. Intuitive car infotainment solutions enable the user to explore maps, toggle menus and radio stations using simple gesture control.
There are many challenges associated with the accuracy and usefulness of gesture recognition software. For image-based gesture recognition there are limitations on the equipment used and image noise. Images or video may not be under consistent lighting, or in the same location. Items in the background or distinct features of the users may make recognition more difficult.
The variety of implementations for image-based gesture recognition may also cause issue for viability of the technology to general usage. For example, an algorithm calibrated for one camera may not work for a different camera. The amount of background noise also causes tracking and recognition difficulties, especially when occlusions (partial and full) occur. Furthermore, the distance from the camera, and the camera's resolution and quality, also cause variations in recognition accuracy.
In order to capture human gestures by visual sensors, robust computer vision methods are also required, for example for hand tracking and hand posture recognition or for capturing movements of the head, facial expressions or gaze direction.
"Gorilla arm" :"Gorilla arm" was a side-effect of vertically oriented touch-screen or light-pen use. In periods of prolonged use, users' arms began to feel fatigue and/or discomfort. This effect contributed to the decline of touch-screen input despite initial popularity in the 1980s. In order to measure arm fatigue and the gorilla arm side effect, researchers developed a technique called Consumed Endurance.
The market is changing rapidly due to evolving technology and more and more OEM’s are moving towards gesture recognition technology adoption. As per a report published by Markets and Markets, the gesture recognition market is estimated to grow at a healthy CAGR from 2013 till 2018 and is expected to cross $15.02 billion by the end of these five years. Analysts forecast the Global Gesture Recognition market to grow at a CAGR of 29.2 percent over the period 2013-2018.
If we talk in terms of industry, then currently consumer electronics application contributes to more than 99% of the global gesture recognition market. As per the report published, the Healthcare application is expected to emerge as a significant market for gesture recognition technologies over the next five years. The automotive application for gesture recognition is expected to be commercialized in 2015.
Factors driving the need for a Solution
There are many solutions provided by top vendors which cater to the business need of gesture recognition. Each solution comes with one or more shortcomings. The solutions available are closely coupled with the underlying hardware. It allows almost no flexibility for using it with any other hardware. This is one of the major factors which makes it impossible to port gesture recognition application developed on one platform to another.
Solutions available in the market today come up with a list of pre-defined cameras. The camera interface is either integrated into the solution or is supported by the solution. Gesture recognition applications built using these solutions
are bound to use the specified set of cameras. The applications are highly dependent on camera capability and the application development is limited by the set of features camera is offering.
There could be a situation where the hardware (camera) is already in place but the solution required to build the gesture recognition application does not support the camera which results in extra hardware cost. Today’s solutions are highly dependent on camera and do not allow the flexibility to use any other camera.
Gesture recognition is spanning its wings across various industries like automobile, healthcare, media etc. Solutions available today are targeting specific market area. The leading vendors are offering solutions which cater to the need of specific OEM. If the solution is designed for developing gesture recognition application for automobile, it will have the SDK designed specifically for automobile gesture recognition applications. If a solution is targeting healthcare, it will have gesture detectors specifically for developing healthcare gesture recognition applications.
Upcoming New Technologies
The Sixth Sense Device:-
SixthSense is a wearable gestural interface device developed by Pranav Mistry, a PhD student in the Fluid Interfaces Group at the MIT Media Lab. IT is similar to Telepointer, a neckworn projector/camera system developed by Media Lab student Steve Mann (which Mann originally referred to as "Synthetic Synesthesia of the Sixth Sense"). The SixthSense prototype is comprised of a pocket projector, a mirror and a camera. The hardware components are coupled in a pendant like mobile wearable device. Both the projector and the camera are connected to the mobile computing device in the user‟s pocket. The projector projects visual information enabling surfaces, walls and physical objects around us to be used as interfaces; while the camera recognizes and tracks user's hand gestures and physical objects using computer-vision based techniques. The software program processes the video stream data captured by the camera and tracks the locations of the colored markers (visual tracking fiducials) at the tip of the user‟s fingers using simple computer-vision techniques. The movements and arrangements of these fiducials are interpreted into gestures that act as interaction instructions for the projected application interfaces. The maximum number of tracked fingers is only constrained by the number of unique fiducials, thus SixthSense also supports multi-touch and multi-user interaction. The SixthSense prototype implements several applications that demonstrate the usefulness, viability and flexibility of the system. The map application lets the user navigate a map displayed on a nearby surface using hand gestures, similar to gestures supported by Multi-Touch based systems, letting the user zoom in, zoom out or pan using intuitive hand movements.
The drawing application lets the user draw on any surface by tracking the fingertip movements of the user‟s index finger. SixthSense also recognizes user‟s freehand gestures (postures). For example, the SixthSense system implements a gestural camera that takes photos of the scene the user is looking at by detecting the „framing‟ gesture. The user can stop by any surface or wall and flick through the photos he/she has taken. SixthSense also lets the user draw icons or symbols in the air using the movement of the index finger and recognizes those symbols as interaction instructions. For example, drawing a magnifying glass symbol takes the user to the map application or drawing an „@‟ symbol lets the user check his mail. The SixthSense system also augments physical objects the user is interacting with by projecting more information about these objects projected on them.
Construction and Working: -
The SixthSense prototype comprises a pocket projector, a mirror and a camera contained in a pendant like, wearable device. Both the projector and the camera are connected to amobile computing device in the user‟s pocket. The projector projects visual information enabling surfaces, walls and physical objects around us to be used as interfaces; while the camera recognizes and tracks user's hand gestures and physical objects using computer-vision based techniques. The software program processes the video stream data captured by the camera and tracks the locations of the colored markers (visual tracking fiducials) at the tips of the user‟s fingers. The movements and arrangements of these fiducials are interpreted into gestures that act as interaction instructions for the projected application interfaces. SixthSense supports multi-touch and multi-user interaction.
Replaces large I/P devices. Also usefull for Physically handicapped person. It provides a simple, usable and interesting user
interface and satisfies the need for more freedom in a human computer interaction environment.
It is considered as a powerful tool for computers to begin to understand human body language.
It is widely used in various application areas since it gives the user a new experience of feeling.
According to the Markets and Markets analysis, the growth of gesture recognition is going to be huge. So, we have a huge opportunity to play in this technology. The end user is now moving towards a whole new path of human machine interaction. This is creating a demand for enabling gesture recognition in every facet of market. The solution we are proposing will have a mammoth place in gesture recognition market. Using the solution, we can develop easily and quickly gesture recognition applications for various industries. The solution will aim to develop business tie-ups with major OEMs.