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HDL Implementation of
Object Tracking throughKalman Filtering
Guide
Prof. P.J Engineer Prepared by
Parthiv Bharti
P09 EC 916
Co-Guide
Prof. M.C Patel
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Overview
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Applications of Object Tracking
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Complexities Involved
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Complex object shapes and motions
Non Rigid or articulated nature of Objects
Partial or full object occlusions
Changes in illumination levels
Real Time Processing requirements
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Assumptions that Simplify«..
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Object motion is smooth
No abrupt changes in shape
Constant velocity and/or acceleration in motion
Prior knowledge about shape and size
Appearance and illumination do not vary
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Status
Introduction
Object Representation and FeatureSelection
Object Detection
Object Tracking
Issues and Future Directions
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Object Representation
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Point Representation
Primitive Geometric Shapes
Object Silhouette
Skeletal Models
Articulated Shape Models
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Appearance Representations
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Characterize an image region by it statistics.
If the statistics differ from background, they
should enable tracking.
Templates
Simple geometric shapes/silhouettes
whose poses do not vary considerably
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Choosing Representations
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Point representations for very small objects
Primitive shapes are results of approximations
For complex objects Silhouette scores
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Feature Selection
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Colour
Edges
Texture
It is measure of intensity variation
of a surface which quantifies
properties such as smoothness
and regularity.
Less sensitive to illumination
changes
Influenced by illumination
variation
In general, the most desirable property of a visual feature is its uniqueness so that
the objects can be easily distinguished in
the feature space
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Choosing Features
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Choice is application dependent
Colour is the most popular one
Automatic feature selection is the future
Combination of various features improves
performance
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Status
Introduction
Object Representation and FeatureSelection
Object Detection
Object Tracking
Issues and Future Directions
12
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Object Detection
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Blobs
In every frame
Use of temporal information reduces false
detection
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Point Detection
14find interest points in images which have an expressive
texture in their respective localitiesMoravec¶s Interest Operator
Harris Interest Operator
Kanade-Lucas-Tomasi [KLT] Detector
Scale Invariant Feature Transform [SIFT]
Detector
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Moravec¶s Operator
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Compute variation of image intensities in a 4x4 patch
in horizontal, vertical, diagonal, and anti- diagonal
directions
Select the minimum of four variations to represent a
window
An interesting point is a local maximum in 12x12
patch
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Harris Detector
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Compute the first order derivates in X and Y
directions
Second moment matrix is evaluated for each pixel
in a small neighbourhood
An interest point is identfied using determinant
and trace of this matrix
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SIFT Detector
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Produce image with different scales
SIFT feature descriptor is invariant to scale, orientation,
and affine distortion, and partially invariant to illumination
changes
Convolve each with Gaussian kernel
The differences between adjacent scales of convolved images are calculated
Candidate keypoints are local maxima and minima of the
difference
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Segmentation and Background
Subtraction18
Any significant change in an image region from the
background model signifies a moving object
Segmentation partitions the image into perceptually
similar regions
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Status
Introduction
Object Representation and FeatureSelection
Object Detection
Object Tracking
Issues and Future Directions
19
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Object Tracking
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Generate the trajectory for an object over time by
locating its position in every frame of the video
Real Time Constraint
Use only a small portion of the model space toreduce the computational burden
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Point Tracking
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Objects represented by points
Point matching is done
External Point detecting mechanism required
Eg :Kalman Filter, Particle Filter
Point Tracking
Constraints upon Movement
Possible Paths
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Kernel Tracking
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Kernel = object shape and appearance
E.g. A rectangular template or an elliptical shape with
an associated histogram
Objects are tracked by computing the motion (parametrictransformation such as translation, rotation, and affine) of
the kernel in consecutive frames
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Mean Shift Tracking
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Objects are represented by their color-histograms
Iteratively compares the histogram of the original
object in the current frame and that of candidate
regions in the next frame of image.
The aim is to find maximum correlation between the two
histograms
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Mean Shift Tracking
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Silhouette Tracking
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Uses the information encoded inside the object region
(appearance density and shape models)
Silhouettes are
tracked by
Shape Matching
Contour Evolution
Difficulty
Object Split and Merge
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Graph-based Tracking
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Graphs offer a way to represent the structure in a rich
and compact manner
Node attributes: size, average color, position
Edges specify the spatial relationships(adjacency,
border) between the nodes
In this way, each image of a sequence is segmented and
represented as a region adjacency graph
Object tracking becomes a graph-matching problem
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Status
Introduction
Object Representation and FeatureSelection
Object Detection
Object Tracking
Issues and Future Directions
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Issues
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Resolving Occlusions
Multiple Camera Tracking
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Future Directions
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De elo ent of ro oust tr c ers in e l i e
n iron ent
r c ing in unconstr ined ideos
Multi le Peo le r c ing
Co ining udio long with ideo to
o erco e occlusion
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Status
Introduction
Object Representation and FeatureSelection
Object Detection
Object Tracking
Issues and Future Directions
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