Precision tracking with segmentation for imaging sensors

Precision target tracking based on data obtained from imaging sensors when the target is not fully visible during tracking is addressed. The image is divided into several layers of gray level intensities and thresholded. A binary image is obtained and grouped into clusters using image segmentation. The association of the various clusters to the track to be estimated relies on both the motion and pattern recognition characteristics of the target. The centroid measurements of the clusters and the probabilistic data association filter (PDAF) are employed for state estimation. Expressions for the single-frame-based centroid measurement noise variance of the target cluster and the optimal parameters for cluster segmentation are given. Simulation results validate the expressions for the measurement noise variance as well as the performance predictions of the tracking method. For a dim synthetic target with strong background noise, subpixel accuracy in the range of 0.3-0.4 pixel RMS error with moderate

[1]  H. M. Shertukde,et al.  Tracking of crossing targets with imaging sensors , 1991 .

[2]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[3]  Peter Maybeck,et al.  An Adaptive Extended Kalman Filter for Target Image Tracking , 1981, IEEE Transactions on Aerospace and Electronic Systems.

[4]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[5]  Peter S. Maybeck,et al.  Enhancements to a multiple model adaptive estimator/image-tracker , 1988 .

[6]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[7]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[8]  Peter Maybeck,et al.  A target tracker using spatially distributed infrared measurements , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[9]  Krishna R. Pattipati,et al.  Use of measurements from an imaging sensor for precision target tracking , 1989 .

[10]  Brian Everitt,et al.  Clustering of large data sets , 1983 .

[11]  Y. Bar-Shalom,et al.  Tracking of splitting targets in clutter using an interacting multiple model joint probabilistic data association filter , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[12]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[13]  Rama Chellappa,et al.  Estimation of Object Motion Parameters from Noisy Images , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Peter Maybeck,et al.  Adaptive Tracker Field-of-View Variation Via Multiple Model Filtering , 1985, IEEE Transactions on Aerospace and Electronic Systems.