以適應性門檻值為基礎應用於物件偵測之 強化切割演算法

An Enhanced Segmentation Algorithm Based on Adaptive Threshold for Object Detection

陳俊廷(Chun-Ting Chen)

指導教授 ： 蘇崇彥

國立臺灣師範大學/科技學院/應用電子科技學系/碩士(2011年)

摘要

物件偵測演算法是應用於智慧型影像系統中的基礎研究，尤其大量被用於智慧型監控系統與智慧型傳輸系統上，目的在於透過有效的偵測物件，使其資訊能夠提供給物件追蹤或辨識等相關應用，提升系統之效能，一般而言，大部分的研究常使用背景相減法，利用畫面之間的差異來擷取物件，而這樣的方式通常需要訂定門檻值將影像中的資訊分類，可是在傳統的作法中，此門檻值通常為定值，所以常需要因為測試環境的不同而重新定義，此外，陰影成份也是一項影響物件偵測演算法準確率的因素之ㄧ，所以在將影像資訊分類後，必須再透過陰影去除相關之演算法，將陰影成份去除以取出正確的物件資訊，所以在本論文中，我們提出一個根據陰影成份的特性分析，以像素點為基礎，個別定義其適應性之門檻值應用於物件偵測之演算法，使物件切割與陰影去除的動作，只要透過一個步驟的強化切割演算法，便能夠即時處理並準確的偵測物件，並且能夠將陰影成份的干擾降到最低，使偵測出來的物件資訊能夠更加的與實際物件吻合，實驗結果顯示，即使是在室內、戶外或是雨天的環境下，透過我們所提出的方法也都能夠快速並有效的偵測物件。

關鍵字

適應性門檻值 ； 強化切割 ； 背景相減法 ； 陰影去除 ； 物件偵測

並列摘要

Object detection Algorithm is a fundamental research for intelligent video system. It is widely used in intelligent surveillance system and Intelligent Transportation System (ITS). The purpose is to improve the performance of the system for object tracking or object recognition by detecting object effectively. In general, most of the research is using the difference between the frames to detect objects by background subtraction method. But it needs to set a fixed threshold to classify objects and background in traditional methods. It must redefine the threshold value when the testing environments are changed. Furthermore, shadow component is also a factor to interfere with the correctness of object detection algorithm. It needs to use a shadow removal algorithm to refine the parts of object after classifying objects and background.
In this thesis, we present an enhanced segmentation algorithm with a pixel-dependent threshold for locating shadow regions according to the characteristic of shadow to reduce the interference of shadows and segment objects effectively in real-time. With that, the shadow regions are located more accurately and the moving objects are extracted more completely. Experimental results verify the proposed approach and show that it is helpful for object detection in various environments.

並列關鍵字

adaptive threshold ； enhanced segmentation ； background subtraction ； shadow removal ； object detection

參考文獻

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