Real-time motion tracking of three-dimensional objects

Verghese, G.; Gale, Karey Lynch; Dyer, Charles R.

doi:10.1109/robot.1990.126299

Cited by 16 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In separate work, Gennery (1981) also examined the probabilistic evaluation of feature matches to a model. Verghese et al (1988;) describe a system for real-time tracking of rigid 3-D objects, based on the assumption that features are spatiotemporally dense (i.e., move less then one pixel from frame to frame). Bray (1990) has developed a system that individually tracks each image edge over short distances and uses the motion of these individual edges to solve for combined object motion.…”

Section: Previous Approachesmentioning

confidence: 99%

Robust model-based motion tracking through the integration of search and estimation

Lowe

1992

Int J Comput Vision

279

118

View full text Add to dashboard Cite

A computer vision system has been developed for real-time motion tracking of 3-D objects, including those with variable internal parameters. This system provides for the integrated treatment of matching and measurement errors that arise during motion tracking. These two sources of error have very dierent distributions and are best handled by separate computational mechanisms. These errors can be treated in an integrated way by using the computation of variance in predicted feature measurements to determine the probability of correctness for each potential matching feature. In return, a best-rst search procedure uses these probabilities to nd consistent sets of matches, which eliminates the need to treat outliers during the analysis of measurement errors. The most reliable initial matches are used to reduce the parameter variance on further iterations, minimizing the amount of search required for matching more ambiguous features. These methods allow for much larger frame-to-frame motions than most previous approaches. The resulting system can robustly track models with many degrees of freedom while running on relatively inexpensive hardware. These same techniques can be used to speed verication during model-based recognition.

show abstract

Section: Previous Approachesmentioning

confidence: 99%

Robust model-based motion tracking through the integration of search and estimation

Lowe

1992

Int J Comput Vision

279

118

View full text Add to dashboard Cite

show abstract

“…They require additional information from a world model in order to differentiate between intensity differences due to changes in viewer motion and intensity changes produced by object performed in either a two-dimensional [9] or three-dimensional [12] [19] frame-of-reference. A useful survey of work done using these methods can be found in [20]. Three A more efficient way of using a three-dimensional frame of reference for model matching involves computing only the changes in structure position and orientation between image frames [20].…”

Section: Introductionmentioning

confidence: 99%

“…A useful survey of work done using these methods can be found in [20]. Three A more efficient way of using a three-dimensional frame of reference for model matching involves computing only the changes in structure position and orientation between image frames [20]. [20].…”

Section: Introductionmentioning

confidence: 99%

“…Three A more efficient way of using a three-dimensional frame of reference for model matching involves computing only the changes in structure position and orientation between image frames [20]. [20]. refers to the intensity function at a pixel located at position (x,y) I(x,y) = G* (x,y)j-G* (x,y)j^j [1] All non-moving features in the image disappear in this difference image since the grayscale value of a pixel in the second frame is being subtracted If this condition is satisfied, the distance d is computed between the point at image coordinate (xj, yj) and each of k lines used to define the object model using equation [4]:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-time model-based tracking combining spatial and temporal features

Chaconas¹,

Nashman²

1991

View full text Add to dashboard Cite

“…Luo, Mullen and Wessel [24] report a real-time implementation of motion tracking in 1-D based on Horn and Schunk's method. Verghese et al [33] report real-time, short-range visual tracking of objects using a pipelined system similar to our own. Safadi [30] uses a tracking filter similar to our own and a pyramid based vision system, but few results are reported with this system.…”

Section: Introductionmentioning

confidence: 87%

Real-time visual servoing

Allen

Yoshimi

Timčenko

Proceedings. 1991 IEEE International Conference on Robotics and Automation

129

View full text Add to dashboard Cite

This paper describes a new real-time tracking algorithm in conjunction with a predictive filter to allow real-time visual servoing of a robotic arm that is following a moving object. The system consists of two calibrated (but unregistered) cameras that provide images to a real-time, pipelined-parallel optic-flow algorithm that can robustly compute optic-flow and calculate the 3-D position of a moving object at approximately 5 Hz rates. These 3-D positions of the moving object serve as input to a predictive kinematic control algorithm that uses an α − β − γ filter to update the position of a robotic arm tracking the moving object. Experimental results are presented for the tracking of a moving model train in a variety of different trajectories.

show abstract

Real-time motion tracking of three-dimensional objects

Cited by 16 publications

References 11 publications

Robust model-based motion tracking through the integration of search and estimation

Robust model-based motion tracking through the integration of search and estimation

Real-time model-based tracking combining spatial and temporal features

Real-time visual servoing

Contact Info

Product

Resources

About