Object Tracking in Video Sequence Using Modified Kalman Filter with a Shrinking Active Contour as a Measuring Tool

Document Type


Degree Name

Master of Science (MS)


Computer Science and Info Sys

Date of Award

Fall 2013


Object tracking in video is an important ongoing research with application in the field of human object interaction recognition in video sequences and other multimedia. In the present study, we employ the Kalman Filter, which provides a method capable for predicting and estimating the mass center position of the object in the next frame. Further, we have elaborated and modified the Kalman Filter such that to apply parametric shrinking active contour (S-ACES) with a large capture range as a segmenting tool to determine the location of the object in the present frame. This location is used to predict object's position in the next consecutive frame of a video sequence by using the Modified Kalman algorithm. The Predicted position results may contain some error. To minimize error in prediction, we introduce an estimated position that provides object's more accurate location in the next consecutive frame. A number of experiments are performed with video clips to validate the theory in a way that keeps the accuracy of tracking.


Nikolay Metodiev Sirakov

Subject Categories

Computer Sciences | Physical Sciences and Mathematics