基於多團塊模型及平均移動法之物體追蹤技術

Abstract

在本文中，我們提出一套能夠自動偵測畫面中移動物體並持續追蹤的演算法，並嘗試解決在物體追蹤問題中常遭遇的遮蔽、場景變化以及光源變化等等問題。我們自行定義一個多團塊模型用以描述移動物體，並基於多團塊模型定義適合的相似性度量，進而發展出以平均移動法為基礎的追蹤系統。我們也針對移動物體在畫面中的大小以及方向性提出一套調整方式。整個系統還包括模型更新、目標丟失等等判斷機制，讓追蹤結果更加合理、強韌。實驗結果顯示我們提出來的演算法在室內、室外等不同場景都能夠正確地追蹤移動物體的運動行為。

In this thesis, we proposed an object tracking system, which can automatically detect a single moving object in an image sequence and keep tracking of this object. In the proposed system, we deal with the problems of occlusion, scene change and luminance change. A multi-blob model is defined in our approach to represent the moving object. With this multi-blob model, we proposed a new similarity measure and developed a new object tracking algorithm based on the mean-shift method. We also proposed a strategy to update the size and orientation of the bounding ellipse of the moving object. For the sake of robustness, the proposed system contains decision criteria to handle model updating and loss of target. Simulation results demonstrate that the proposed object tracking algorithm can faithfully track the moving object in different scenes.