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
