Stereo Object Detection and Tracking Using Clustering and Bayesian FilteringStereo Object Detection and Tracking Using Clustering and Bayesian FilteringTexas Tech University 2011 NSF Research Experiences for Undergraduates Site ProjectTexas Tech University 2011 NSF Research Experiences for Undergraduates Site ProjectJames SmithJames SmithFaculty Advisor: Dr. Mohan SridharanFaculty Advisor: Dr. Mohan Sridharan

Abstract

Robots equipped with sensors are being increasingly deployed in real-world scenarios

Vision is a rich source of information for a mobile robot compared to other sensors

Algorithms to process visual inputs computationally expensive

Primarily focused on implementing image clustering to detect objects

Secondary research into applying Bayesian filtering to object tracking

Introduction

Methods - Clustering

The process of clustering, or grouping, has long been used in image analysis. The process allows simple object grouping, usually based on various similarities between pixels. We applied a generic clustering algorithm to add disparity as a third dimension.

Search radius around each point to determine similar points

Group similar points as potential objects

Similar process to K-mean clustering

Provides rough estimate of objects in 3-dimensional space

*This research is supported by NSF Grant No. CNS 1005212. Opinions,findings, conclusions, or recommendations expressed in this paper arethose of the author(s) and do not necessarily reflect the views of NSF.

Stereo Imaging and Clustering

Left Stereo Image Right Stereo Image

Disparity Image Clustered ImageErratic Robot

Wheeled

On-board Computer

Battery Operated

Stereo Cameras

Back-Facing Camera

Laser Range-Finder

Clustering disparity images allows quick and accurate object detection.

Research into Bayesian filtering shows promising outcomes in object tracking

Conclusion

Future Work

Combine clustering with other techniques to improve the accuracy of object detection.

Implementation of a Bayesian filtering system to track objects through time.

Eventual integration with other sensor systems to produce more intelligent robots

Bayesian FilteringBayesian filtering works on the principal of creating a probabilistic prediction of future values of data, and correcting those predictions based on how closely the prediction matches reality.

Estimates state through time

Takes various sources of error into consideration

Easily modifiable to trade off speed and accuracy

Sources Greg Welch and Gary Bishop An Introduction to the Kalman Filter University of North Carolina, 2001.

Sebastian Thrun, Wolfram Burgard, and Dieter Fox Probabilistic Robotics Cambridge, MA: MIT, 2005

Nikos Vlassis, Aristidis Likas, and Jakob Verbeek The Global K-Means Clustering Algorithm Pattern Recognition: Vol. 36 Issue 2, 2003

Stereo imaging has been used recently as an effective method of providing distance information in robotic applications. Previously with single image technology, many techniques were created to find and track objects. Our research consisted of applying these techniques to a stereo-vision system.

Object

Distance

Right Camera

Theoretical Distance = ( Focal Length )( Base ) ( Disparity )Base

bel(xt) = ∫ P(x

t | u

t, x

t-1) bel(x

t-1) dx

t-1

bel(xt) = ŋ P(z

t | x

t) bel(x

t)

Left Camera

x = Stateu = Control / Motionz = Observation

Disparity-Distance RelationDisparity values are created on requested by the stereo-on-chip camera. An equation relating disparity to physical distance was determined experimentally.

0.00000 2.00000 4.00000

0.00000

0.50000

1.00000

1.50000

2.00000

2.50000

3.00000

3.50000

f(x) = 1.14932650168768 x + 0.020167573687504

Column J

Linear (Column J)

Real Distance = (591) + .02 Disparity

Real Distance = m(Theoretical Distance) + B

*m, B = Constants