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Object Tracking Strategies

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HDL Implementation of Object Tracking through Kalman Filtering Guide Prof . P .J Engi neer Prepared by Parthiv Bharti P09 EC 916 Co-Guide Prof. M.C Patel
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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

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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

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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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