Feature Based Tracking

Introduction

Continuing Work

Goal

Automatically track vehicles and other objects of interest in

wide area motion imagery.

Constraints/Challenges

Very low resolution, presence of noise, illumination

variation, occlusions, complex object motion, complex object

shapes.

Methodology

Dense feature matching is used to match targets across

frames. Super resolution increases the amount of pixels on a

specific target. Enhancement algorithms improve the quality

of target pixels for better matching.

Background Subtraction

Super-Resolution and Enhancements

Complete Framework for Tracking

Combine both kinematic and feature based tracking

methods for a more accurate and complete algorithm.

Image Registration

Goal

Align two images based on the

similarity between them.

Stages

1. Extract Harris Corner feature

points.

2. Generate SIFT descriptor for each

point.

3. Match features between frames.

4. Filter outliers using RANSAC.

5. Fit planar homography.

Kinematic Blob Tracking

Object Detection

Feature Extraction

Dense SIFT features for every pixel on target.

Search Criteria

Extract features within search region around target in next

frame.

Matching Criteria

Match features from target with search area features to find

target location.Detection

Use background subtraction

frames for detections.

Kalman Filter Predictor

Prediction component based on

past detection of target.

Tracking

Detections linked based on

predicated locations and

detections.

1st Frame 3rd Frame 20th Frame

Original Image Median Image Original – Median

Image: No Gradient

Suppression

Gradient Suppression:

Background

Subtraction Image

Input

Frames

Kinematic

Tracking

Feature

Tracking

Update

all Tracks

Registration and

Background

Subtraction

Regular Matching Super Resolution +

Enhancement Matching

Dense SIFT Feature Extraction