This document discusses finger tracking techniques. It begins with an introduction to finger tracking and its uses in technology. It then discusses different types of finger tracking, including those that use interfaces like gloves and those that track fingers without interfaces. The document outlines an algorithm for finger tracking and describes test sequences used to evaluate the algorithm. It concludes by discussing applications of finger tracking and its future potential to replace devices like mice.

1. FINGER TRACKING
PRESENTED BY:
V JASWANTH
11416011
COMPUTERSCIENCEAND
ENGINEERING

2.  ABOUT
 INTRODUCTION
 TYPES OF TRACKING
 ALGORITHM
 TEST SEQUENCES
 APPLICATIONS
 CONCLUSIONMARY

3.  In the field of technology and image processing , finger
tracking is a high-revolution technique that is employed to
know the consecutive position of the user and hence
represent objects in 3D.
 In addition to that , the finger tracking technique is used as
a tool of the computer , acting as an external device in our
computer , similar to a keyboard and a mouse.

4.  The finger tracking system is focused on user-data
interaction, where the user interacts with virtual data, by
handling through the fingers the volumetric of a 3D object
that we want to represent.
 This system was born based on the human-computer
interaction problem.
 The objective is to allow the communication between
them and the use of gestures and hand movements to be
more intuitive, Finger tracking systems have been created.

5.  TRACKING WITH INTERFACE
 Inertial motion capture gloves
 Hand skeleton
 Fusing data with optical motion capture systems
 Hand position tracking
 Optical motion capture systems
 Markers
 Occlusion as an interaction method
 Marker functionality
 Articulated hand tracking

6.  TRACKING WITHOUT INTERFACE
 This model can be applied to a more reduced scale to
describe hand motion and based on a wide scale to
describe a complete body motion.
 A certain finger motion, for example, can be recognized
from its usual angles and it does not depend on the
position of the hand in relation to the camera.

7. An algorithm for finger detection and tracking in
video sequences captured with a common webcam.
 The human beings are using gestures to communicate
with each other. Therefore a gesture based interface
may represent a natural method for the human user to
interact with machines, too.

8.  A vision based gesture recognition system is usually
composed of three main components: image pre-
processing, tracking the significant body elements (e. g.
hands, fingers, face) and gesture recognition.
 Although a lot of research has been conducted in the field
of hand and finger tracking, the problem is still a
challenging one in the vision based research area.
 This finger tracking algorithm is suitable for usage in
various vision based human computer interfaces
ranging from pointing type interfaces-using the finger
similar to a mouse to more complex dynamic gesture
based interfaces

9.  The proposed tracking algorithm is designed as a
cascaded multilayer system with a computationally
effective architecture.
 A multi-cue approach – based on foreground-
background segmentation, skin colour segmentation,
objects shape and dimensions proportionality – is used
for finger detection and tracking in order to achieve
robustness.
 The proposed method provides reliable finger
trajectories without having any special hardware
equipment requirements – the hardware system used
for testing the algorithm consists of a low-end
computer and a common webcam.

10.  Hand tracking performed using a single camera is still
attracting for researchers in the field of vision based human
computer interaction given the current ubiquity of webcams,
which enable a potential development of applications based on
this type of tracking to a wide variety of devices, including the
mobile ones.
 Skin colour is widely used for tracking human body parts.
Many skin detection algorithms are available in the literature,
using various skin classification strategies in a wide variety of
colour spaces contains a good survey on the performances of
skin colour classification methods in different colour spaces.

11.  The proposed method was developed for tracking the
index finger in order to provide the obtained trajectory
for dynamic gesture recognition, or mouse-like
pointing interfaces.
 The tracking algorithm relies on multiple
characteristics (features) of the finger: foreground
object, colour, shape, proportionality.
 The proposed finger tracking algorithm uses a multi-
layer approach as shown in Fig.
 The lower layers are responsible with the finger
detection, while the topmost layer is recording the
trajectory and guiding the tracking process.

12. Image pre-
processing
Scan for line
strips
Line strips pre
clustering
Determine
values
Merge
clusters
Identify the
tracked finger
Trajectory recording and
tracker guiding
Trajectory processing
and gesture recognition
layers
Fig. The layers of the tracking algorithm

13.  The image pre-processing extracts the low-level features
(foreground object and skin colour) through background
subtraction and skin colour segmentation.
 Which significantly reduces the search area and the data to
process, but is not sufficient to uniquely identify the finger
–the finger is often not the only foreground object in the
image. For skin segmentation, our choice is a threshold
based method, applied in both RGB and HSV colour
spaces.

14.  Following the pre-processing steps, the remaining significant
data are further refined through 5 processing layers for finger
detection.
 At the basic layer, a search area – selected using data from the
tracker guiding layer –is scanned horizontally or vertically for
line strips. For each line strip only the length and the
midpoint’s coordinates need to be retained, leading to a very
compact representation of the data.
 At the second layer, the line strips are clustered based on
spatial contiguousness and displacement, allowing only small
gaps between line strips in order to deal with noisy images.
Isolated clusters are removed, as they contain no useful data.

15.  At the third layer a pair of values are determined for each
line strip.
 At the fourth layer the clusters are checked for the presence
of a hand at one of the ends – a finger normally appears
only as an extension of a hand.
 The search for the presence of a hand starts at the base of
the finger candidate and tries to find an area delimited by a
polygonal contour which contains a large majority of skin
coloured, foreground pixels.
 The fifth layer identifies the tracked finger amongst the
finger candidates selected at the previous layer.

16.  The topmost layer of the tracking algorithm is using
the data provided by the lower layers in order to
perform 2 tasks: finger trajectory recording –
providing the data necessary to the gesture recognition
layers – and tracker guiding.
 The data necessary to the gesture recognition layers
consists of the finger position, orientation and
optionally size in every frame.
 The data required for tracker guidance consist of a
predicted position and size of the finger in the next
frame, based on which a search window is selected in
order to apply the finger detection steps.

17.  The proposed tracking algorithm was tested on 8 video
sequences taken in different conditions. The test
sequences were acquired both under natural and artificial
lighting, including simple and complex background, hand
movement at various speeds, movement speed variation,
partial occlusion with other objects, hand pose changes,
superposing with other foreground objects, including
hands with extended index finger

18. Finger tracking in the absence of disturbances
Fig. Tracking at fast motion speed
Tracking the finger and the hand when another hand with an
extended finger is in the neighbourhood of the target

19. Tracking the finger while crossing the face area
When total occlusion with another finger
Fig. Tracking the finger during total occlusion with
another finger

20.  Definitely, the finger tracking systems are used to
represent a virtual reality.
 However its application has gone to professional
level 3D modelling, companies and projects directly in
this case overturned. Thus such systems rarely have
been used in consumer applications due to its high
price and complexity.
 In any case, the main objective is to facilitate the task
of executing commands to the computer via natural
language or interacting gesture.

21.  The finger tracking is already in use but they are
using interfaced technologies. The researches are
going on to come up with efficient algorithms
which doesn’t use interface. In future, you may not
be surprised to receive your computer with already
pre loaded finger tracking algorithm instead of
external devices such as mouse.