Motion estimation and detection of complex object by analyzing resampled movements of parts

Abstract

A moving object that has many complex moving parts is very hard to detect and its motion is not easy to estimate. We present a new technique for motion estimation and detection of moving complex objects by analyzing the resampled motions of parts of the objects. A Kalman filter is used to track all resampled movements and the tracked routes are classified into groups that share the same fundamental movements. Our simulations show that recall of motion estimation and detection is approximately 0.8, while the computation drops exponentially.