基於色彩及深度影像之即時人形偵測系統設計

Abstract

本篇論文針對人形偵測提出一個即時人形偵測系統，此系統基於Kinect所產生的彩色及深度串列影像來找出影像中的人形。此系統分成四個部分，包括感興趣區域的選擇、特徵擷取、人形識別以及靜態人形之檢查。首先，根據人在行走或站立時之人形特性，進而利用直方圖投影、連通物件標示法以及移動物體區分法來選擇出感興趣的區域。然後，藉由雙線性插值法將感興趣的區域標準化，以及透過方向梯度直方圖來擷取人形的特徵。再來是採用支持向量機或類神經網路來訓練出Leeds Sports Pose資料集的分類器，並利用此分類器來辨識人形。最後，檢查在影像中是否有包含任何靜態的人形而後辨識它。從實驗結果可知，本論文所設計之人形偵測系統擁有即時且高準確率的特性。

This thesis proposes a real-time human detection system based on RGB-D images generated by Kinect to find out humans from a sequence of images. The system is separated into four parts, including region-of-interest (ROI) selection, feature extraction, human shape recognition and motionless human checking. First, the histogram projection, connected component labeling and moving objects segmentation are applied to select the ROIs according to the property that human is walking or standing with motion. Second, resize the ROIs based on the bilinear interpolation approach and extract the human shape feature by Histogram of Oriented Gradients (HOG). Then, support vector machine or artificial neural network is adopted to train the classifier based on Leeds Sports Pose dataset, and human shape recognition is implemented by the classifier. Finally, check whether the image contains any motionless human, and then recognize it. From the experimental results, the system could detect humans in real-time with high accuracy rate.