Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction

Abstract

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

A Modified Expansion-Contraction Method for Mobile Object Tracking in Video Surveillance: Indoor Environment

“…The study on the occlusion can be divided into two kinds. The one is using color and shape information [15] and other is movement information of target object by using particle filer or Kalman filter algorithm [16,20]. However, when both the objects have identical or similar colors, and are of similar shape, then the tracking may fail.…”

“…The basic expansion-contraction (E-C) algorithm has been presented in previous papers [16,17]. The problems discussed in these papers include…”