應用於影像追蹤遮蔽問題之改良式粒子濾波器

Abstract

近年來，物體追蹤為一備受關注之議題，在機場、學校、醫院及交通的監控系統等方面都有相當廣的應用，物體監控提供我們物體之間的行為、互動、關係等重要資訊。本論文旨在解決複雜環境下的物體追蹤，提出加入精緻重新取樣法的適應性粒子濾波器追蹤演算法，在預測、重要性取樣、重新取樣三個階段皆有所改善。首先，在預測部分，能夠適應性調整搜索範圍，並在物體消失後調整預測的範圍及取樣點數量，更快速地得到精確的結果；另外，以光流法為輔，以物體動態運動資訊來調整取樣點權重，得更準確的物體位置；最後，加入精緻重新取樣演算法，反映更符合實際權重分布情形，讓重新取樣之結果更為可靠。此方法可應用於固定式及主動式的攝影機，即使在有遮蔽或物體完全消失的情況下，也能準確地判斷，與其他追蹤方法比較，有更準確的結果。

In recent years, the visual object tracking has drawn increasing interests. There are many applications, e.g., video surveillance in airports, schools, hospitals and traffic. The object surveillance may provide crucial information about the behavior, interaction, and relationship between objects of interest. This paper addresses issues in object tracking where videos contain complex scenarios. We propose an adaptive particle filters tracking scheme with exquisite resampling (AERPF), which improves prediction, importance sampling and resampling. In prediction step, an adaptive strategy for search region and particle number is addressed for object disappearing or obstacle disturbance, which can obtain results more effectively. In addition, in importance sampling, we use optical flow to refine the particle weights using the dynamical object motion information, which results the better accuracy of object location updating. Moreover, exquisite resampling (ER) algorithm can be applied for reflecting more the posterior probability density function of true state. The proposed method can be applied for object tracking both on fixed and active camera, handling partial occlusion and full occlusion problem properly. As a result, it outperforms other existing methods.