HSV色彩空間前景物體抽取及其於人體動作辨識系統應用

Abstract

利用串流影像資訊於人類行動辨識能在許多地方應用，如:人機介面、安全監控、居家安全照護等系統，本論文的提出一個可以自動監控、追蹤辨識人類動作的系統。在一般前、後景色彩深淺差別大時，可以簡單的使用亮度的資訊將前後景分離，但當前後景亮度接近時，例如; 當辨識的目標穿著和背景相似的衣服時，若只使用灰階影像並無法將完整的前景資訊分離，因此我們使用HSV色彩空間加入像素點色彩成分的考慮建立背景模型，達到前、後景的分離，且能對陰影的問題加以消除改進。但是使用HSV色彩空間必須先解決色調一些不穩定的問題，所以我們在色調不穩定的區域加以限制，以增加抽取前景影像的準確性。 將抽取的影像以二值化，再將經過特徵空間以及標準空間轉換，投影至標準空間。經由樣板比對的方法將三張影像合為一個姿態變化序列，此影像序列乃從動作視訊5:1減低抽樣獲得。接著，利用模糊法則的推論方法，將這組時序姿態序列分類為某一個動作類別。跟單用亮度成分的方法比較，實驗證明，HSV色彩空間不但在前景影像抽取有明顯的改進,而且在人體動作辨識結果也有顯著的改進。

Human activity recognition from video streams has a wide range of application such as human-machine interface, security surveillance, home care system, etc. The objective of this thesis is to provide a human-like system to auto-survey and then to track people and identify their activities. When the foreground color is different from the background color, the foreground subject can be extracted easily by the luminance component. When the foreground color is similar to the background color, we cannot extract the foreground image completely by the luminance component. To solve this, we utilize the HSV color space to build the background model, in line with similar spirit of W4 segmentation algorithm, which can not only extract foreground image but also be helpful to shadow removal. Since H and S component are not reliable in some conditions, we make use of three criteria to obtain reliable and static hue values. A foreground subject is first converted to a binary image and transformed to a new space by eigenspace and canonical space transformations. Recognition is done in canonical space. A three image frame sequence, 5:1 down sampling from the video, is converted to a posture sequence by template matching. The posture sequence is classified to an action by fuzzy rules inference. In our experiment, extracting the foreground image in the HSV space improves not only the accuracy of foreground image but also human activity recognition accuracy.