[attachment=3228]

OBJECT TRACKING AND DETECTION

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Presented By
Sneha K. Desai
Under the guidance of:
Mr. Anchi. F. B
Dept. of Computer Science,
R.E.C, Hulkoti.

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CONTENTS
1. Introduction to object tracking
2. Object representation
3. Feature selection for tracking
4. Algorithm for object tracking
5. Introduction to object detection
6. Moving object detection algorithms
7. Advantages and disadvantages of moving object detection
8. Conclusion


Object Tracking
A method of following an object through successive image frames
to determine its relative movement with respect to other objects.



Object Representation
In a tracking scenario, an object can be defined as anything that
is of interest for further analysis.
Objects can be represented by their shapes.

Object shape representations commonly employed for tracking are:
¢ Points: The object is represented by a point, that is, centroid or
set of points. Point representation is suitable for tracking objects
that occupy small regions in an image.
¢ Primitive geometric shapes: Object shape is represented by a
rectangle, ellipse etc. these are suitable for representing simple
rigid objects and non rigid objects.

¢ Object silhouette and contour: contour representation defines the boundary of an object. The region inside the contour is called the silhouette of the object. These are suitable for tracking complex non rigid shapes.
¢ Articulated shape models: These objects are composed of body parts that are held together with joints.
¢ Skeletal models: object skeleton can be extracted by applying medial axis transform to the object silhouette. This can be used to model both articulated and rigid objects.



Feature selection for tracking
The common visual features are as follows:
¢ Color: The apparent color of an object is influenced by two factors i.e. spectral power distribution of illuminant and surface reflectance properties.

¢ Edges: Object boundaries generate strong changes in the image intensities. Edge detection is used to identify these changes
¢ Optical Flow: Optical flow is a dense field of displacement vectors which defines the translation of each pixel in a region. It is computed using the brightness constraints.
¢ Texture: Texture is a measure of the intensity variation of the surface which quantifies properties such as smoothness and regularity.




Object Detection
Object detection from video sequence is the process of detecting the moving objects in frame sequence using digital image processing techniques.
Challenges of moving object detection:
¢ Loss of information caused by the 3D world on a 2D image
¢ Noise in images
¢ Complex object motion
¢ Non-rigid or articulated nature of objects
¢ Partial or full object occlusions
¢ Complex object shapes
¢ Scene illumination changes


Frame difference
In this method a background image without any moving objects
of interest is taken as reference image.
Pixel value for each co-ordinate (x, y) for each color channel of the
background image is subtracted from corresponding pixel value of
input image.
If resulting value is greater than a particular threshold value, then
that is foreground pixel otherwise background.




An automatic moving object detection algorithm for video surveillance applications
Steps:
Moving object detection phase
Moving object extraction phase
Moving object recognition phase




Background Subtraction
The moving object regions are detected by subtracting the current
image pixel-by-pixel from a reference background image.
The pixel where the difference is above a threshold are classified
as foreground otherwise background.
Morphological post processing operations are performed to reduce
the effects of noise and enhance the detected object.



FRAME DIFFERENCE AND BACKGROUND SUBTRACTION
The combination of background subtraction and frame differencing can improve the detection speed and overcome the lack of sensitivity of light changes.



Background updating
The background updating of the selected pixels are replaced by the average of the current and background pixels.



conclusion
Object tracking means tracing the progress of objects as they
move about in visual scene.
Object tracking, thus, involves processing spatial as well as
temporal changes.
Certain features of those objects have to be selected for tracking.
These features need to be matched over different frames.
Significant progress has been made in object tracking.
Taxonomy of moving object detection is been proposed.
Performance of various object detection is also compared.

So, atlast it is noted that algorithm based on frame difference and edge detection has detection accuracy and high detection speed

hi mate,
do u hav doc file?
thnx

Hi,
the contents of the doc file are posted in this thread. Please copy it to make your report.

want to get this as a pdf file

[attachment=7428]

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OBJECT TRACKING AND DETECTION

INTRODUCTION TO OBJECT TRACKING

Object tracking is an important task within the field of computer vision. The proliferation of high-powered computers, the availability of high quality and inexpensive video cameras, and the interesting need for automated video analysis has generated a great deal of interest in object tracking.

In its simplest form, tracking can be defined as a method of following an object through successive image frames to determine its relative movement with respect to other objects. In other words, a tracker assigns consistent labels to the tracked objects in different frames of video.

One can simplify tracking by imposing constraints on the motion or appearance of objects. One can further constrain the object motion to be of constant velocity or acceleration based on prior information. Prior knowledge about the number and the size of objects, or the object appearance and shape can also be used to simplify the problem.

Numerous approaches for object tracking have been proposed. These primarily differ from each other based on the way they approach the following question: which object representation is suitable for tracking? Which image features should be used? How should the appearance and shape of object be modelled? The answers to these questions depend on the context/environment in which the tracking is performed. A large number of tracking methods have been proposed which attempt to answer these questions for variety of scenarios.
the schematic of a generic object tracking system. As can be seen, visual input is usually achieved through digitized images obtained from a camera connected to a digital computer. This camera can be either stationary or moving depending on the application. Beyond image acquisition, the computer performs the necessary tracking and any higher-level tasks using the tracking result.OBJECT REPRESENTATION

In a tracking scenario, an object can be defined as anything that is of interest for further analysis. For instance, boats on the sea, fish inside an aquarium, vehicles on a road, planes in the air etc are a set of objects that may be important to track in a specific domain. Objects can be represented by their shapes.

In this section, we will describe the object shape representations commonly employed for tracking.

1. Points:
The object is represented by a point, that is, centroid (fig 2(a)) or by a set of points (fig 2(b)). The point representation is suitable for tracking objects that occupy small regions in an image.

2. Primitive geometric shapes:
Object shape is represented by a rectangle, ellipse (fig 2©, (d)) etc. primitive geometric shapes are more suitable for representing simple rigid objects, they are also used for tracking non rigid objects.

3. Object silhouette and contour:
Contour representation defines the boundary of an object (fig 2(g), (h)). The region inside the contour is called the silhouette of the object (fig 2(i)). Silhouette and contour representations are suitable for tracking complex non rigid shapes.

4. Articulated shape models:
Articulated objects are composed of body parts that are held together with joints. For example, the human body is an articulated object with legs, hands, head feet connected by joints. In order to represent an articulated object, one can model the constituent parts using cylinders or ellipses as shown in fig 2(e).

5. Skeletal models:
Object skeleton can be extracted by applying medial axis transform to the object silhouette. This method is commonly used as a shape representation for recognizing objects. Skeleton representation can be used to model both articulated and rigid objects (fig 2(f)).

Object representations are usually chosen according to the application domain. For tracking object, which appear very small in an image, point representation is usually appropriate. For objects whose shapes can be approximated by rectangle or ellipse, primitive geometric shape representations are more appropriate. For tracking objects with complex shapes, for example, humans, a contour or silhouette based representation is appropriate.


FEATURE SELECTION FOR TRACKING
Selecting the right features plays a critical role in tracking. The most desirable property of visual feature is its uniqueness so that the objects can be easily distinguished in the feature space. In general many tracking algorithms use these features. The details of visual features are:
1. Color: The apparent color of an object is influenced primarily by two physical factors, 1) the spectral power distribution of the illuminant and 2) the surface reflectance properties of the objects. In image processing, the RGB (red, green, blue) color space is usually used to represent color.
2. Edges: Object boundaries usually generate strong changes in image intensities. Edge detection is used to identify these changes. Algorithms that track the boundary of the objects usually use edge as the representative feature.
3. Optical Flow: Is a dense field of displacement vectors which defines the translation of each pixel in a region. It is computed using the brightness constraints, which assumes brightness constancy of corresponding pixels in the consecutive frames.
4. Texture: Texture is the measure of the intensity variation of the surface whitch quantifies properties such as smoothness and regularity.
ALGORITHM FOR OBJECT TRACKING

Background subtraction in video using Bayesian learning:

An accurate and fast background subtraction technique for object tracking in still camera videos. Regions of motion in a frame are first estimated by comparing the current frame to a previous one. A sampling re-sampling based Bayesian learning technique is then used on the estimated regions to perform background subtraction and accurately determine the exact pixels which correspond to moving objects. An obvious advantage in
terms of processing time is gained as the Bayesian learning steps are performed only on the estimated motion regions, which typically constitute only a small fraction of the frame. The technique has been used on a variety of indoor and outdoor sequences, to track both slow and fast moving objects, under different lighting conditions and varying object-background contrast.
This algorithm presents robust system that achieves both (1) high speed and (2) high degrees of sensitivity compared to existing techniques. To achieve these objectives a 2
step tracking system has been used.
1) Motion Region Estimation
2) Bayesian Sampling Resampling

Motion Region Estimation: The Block Matching Algorithm (BMA) is a standard way of encoding video frames. A simplified variation of the BMA algorithm is used for determining regions of each frame which have had motion relative to a reference frame. Such regions have been called regions of motion. Each incoming frame is divided into non-overlapping blocks of equal size. Each block is compared to the corresponding block in the reference frame and the Sum of Absolute Difference (SAD) is determined for the block. The reference frame may be chosen to be a few frames before the current frame, to account for slow moving objects.

plz send me report on object tracking and detection
my e mail is patil9086[at]ymail.com

to get information about the topic "optical flow based object tracking" full report ppt and related topic refer the page link bellow

http://studentbank.in/report-object-trac...ode=linear

http://studentbank.in/report-object-trac...deo-scenes

http://studentbank.in/report-object-trac...ull-report

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you rock bro
thnks for this outstanding effort

(18-04-2010, 07:59 PM)project topics Wrote:

Quote:if u dont mind plzz send me the pdf  

OBJECT TRACKING AND DETECTION

---

Presented By
Sneha K. Desai
Under the guidance of:
Mr.  Anchi. F. B
Dept. of  Computer Science,
R.E.C, Hulkoti.

---

CONTENTS
1. Introduction to object tracking
2. Object representation
3. Feature selection for tracking
4. Algorithm for object tracking
5. Introduction to object detection
6. Moving object detection algorithms
7. Advantages and disadvantages of moving object detection
8. Conclusion


Object Tracking
A method of following an object through successive image frames
to determine its relative movement with respect to other objects.



  Object Representation
In a tracking scenario, an object can be defined as anything that
  is of interest for further analysis.
Objects can be represented by their shapes.
       
Object shape representations commonly employed for tracking are:
¢ Points: The object is represented by a point, that is, centroid or
set of points. Point representation is suitable for tracking objects
that occupy small regions in an image.
¢ Primitive geometric shapes: Object shape is represented by a
 rectangle, ellipse etc. these are suitable for representing simple
 rigid objects and non rigid objects.
         
¢ Object silhouette and contour: contour representation defines the boundary of an object. The region inside the contour is called the silhouette of the object. These are suitable for tracking complex non rigid shapes.
¢ Articulated shape models: These objects are composed of body parts that are held together with joints.
¢ Skeletal models: object skeleton can be extracted by applying medial axis transform to the object silhouette. This can be used to model both articulated and rigid objects.



Feature selection for tracking
The common visual features are as follows:
¢ Color: The apparent color of an object is influenced by two factors  i.e. spectral power distribution of illuminant and surface reflectance properties.
                           
¢ Edges: Object boundaries generate strong changes in the image intensities. Edge detection is used to identify these changes
¢ Optical Flow: Optical flow is a dense field of displacement vectors which defines the translation of each pixel in a region. It is computed using the brightness constraints.
¢ Texture: Texture is a measure of the intensity variation of the surface which quantifies properties such as smoothness and regularity.




Object Detection
Object detection from video sequence is the process of detecting the moving objects in frame sequence using digital image processing techniques.
Challenges of moving object detection:
¢ Loss of information caused by the 3D world on a 2D image
¢ Noise in images
¢ Complex object motion
¢ Non-rigid or articulated nature of objects
¢ Partial or full object occlusions
¢ Complex object shapes
¢ Scene illumination changes


Frame difference
In this method a background image without any moving objects
  of interest is taken as reference image.
Pixel value for each co-ordinate (x, y) for each color channel of the
  background image is subtracted from corresponding pixel value of
  input image.
If resulting value is greater than a particular threshold value, then
  that is foreground pixel otherwise background.
   



An automatic moving object detection algorithm for video surveillance applications
Steps:
Moving object detection phase
Moving object extraction phase
Moving object recognition phase




Background Subtraction
The moving object regions are detected by subtracting the current
   image pixel-by-pixel from a reference background image.
The pixel where the difference is above a threshold are classified
   as foreground otherwise background.
Morphological post processing operations are performed to reduce
   the effects of noise and enhance the detected object.



 FRAME DIFFERENCE AND BACKGROUND SUBTRACTION
The  combination of background subtraction and frame differencing can improve the detection speed and overcome the lack of sensitivity of light changes.



Background updating
The background updating of the selected pixels are replaced by the average of the current and background pixels.



conclusion
Object tracking means tracing the progress of objects as they
  move about in visual scene.
Object tracking, thus, involves processing spatial as well as
  temporal changes.
Certain features of those objects have to be selected for tracking.
These features need to be matched over different frames.  
Significant progress has been made in object tracking.
Taxonomy of moving object detection is been proposed.
Performance of various object detection is also compared.
 
So, atlast it is noted that algorithm based on frame difference and edge detection has detection accuracy and high detection speed

So do we need any tools to implement this,what are they?