以輪廓特徵學習之人類姿勢辨識

Abstract

對一個成功的人類行為分析系統而言，人類姿勢辨識是其中最重要的一部分。在本篇論文中，我們提出以輪廓及機器學習為基礎的方法，以發展出一個有效率且正確性高的人體姿勢辨識系統。在所提出的系統中，我們首先由人體輪廓抽取出其中具有高度識別性的特徵，並利用名為AdaBoost的機器學習方法來建構我們的辨識系統。我們根據人類姿勢中獨有的特性來選擇並修改AdaBoost所使用的特徵。藉由我們所使用的特徵所具的高度描述能力，我們將呈現此系統會較使用傳統步驟的方法有更高的效能。使用者可藉由我們所提出的架構來達到一個快速且有效的人類姿勢辨識。

Human posture analysis is one of the most important steps towards successful human behavior analysis. In this thesis, a silhouette-based learning approach is proposed to develop an efficient and effective human posture recognizing system. Discriminating features from human body silhouette are first extracted and then AdaBoost algorithm is employed for training a recognition system. The features in AdaBoost are selected and modified according to the specific characteristics of human postures. Depending on the describing ability of our features, we demonstrate that our system operates higher performance than the traditional approaches. Users can recognize human postures in an efficient and effective manner using the proposed framework.