利用單眼視覺之增強性方向梯度直方圖於多人偵測

Monocular Multi-Human Detection Using Augmented Histograms of Oriented Gradients

莊振勛(Cheng-Hsiung Chuang)

指導教授 ： 傅立成

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2007年)

https://doi.org/10.6342/NTU.2007.01574

摘要

本篇論文提出利用增強性方向梯度直方圖(Augmented Histograms of Oriented Gradients (AHOG))於移動式平台上進行多人偵測，在本篇研究中，我們利用人體的幾何特徵來加強方向梯度直方圖(Histograms of Oriented Gradients (HOG))描述人型外觀的能力，其中我們把直立人型中存在的對稱性，每個身體部位的相對距離，以及人型在梯度特徵中的密度分佈加入HOG特徵中，來提升HOG特徵的描述能力，包含了上述人型特徵的HOG在此篇研究稱為AHOG，接著利用串接式AdaBoost演算法建立ㄧ個人型串接式分類器，用來對輸入影像中的可能區域進行偵測，由此人型分類器所決定之區域，則被考慮為人型可能區域，除此之外，利用串接式分類器的架構，可以減少偵測人型的時間；最後人型可能區域會再經由人型輪廓驗證，來確信此區域確實有人型存在，並且減少因為由複雜背景所引發的錯誤訊息，藉此降低錯誤偵測的發生。在此研究實驗中，於多種不同的實驗環境中，都可以提供可靠的人型偵測準確率。

關鍵字

人型偵測 ； 方向性梯度直方圖 ； 行人偵測

並列摘要

In this thesis we introduce an Augmented Histograms of Oriented Gradients (AHOG) feature for human detection from a non-static camera. This research tries to increase the discriminating power of original Histograms of Oriented Gradients (HOG) feature by adding human shape properties, such as contour distances, symmetry, gradient density, and shape approximation. The relations among AHOG features are characterized by the contour distances to the centroid of human. By observing on the biological structure of a human shape, we impose the symmetry property on every HOG feature and compute the similarity between feature itself and its symmetric pair so as to weigh HOG features. After that, the capability of describing human features is greatly improved when being compared with that of traditional one, especially when the moving humans are under consideration. Besides, we also augment the gradient density into AHOG to mitigate the influences caused by repetitive backgrounds. Moreover, we reject the false detections via an elliptical verifier learned when one tries to approximate a human shape. In the experiments, our proposed human detection method demonstrates highly reliable accuracy and provides the comparable performance to the state-of-the-art human detector on different databases.

並列關鍵字

human detection ； histograms of oriented gradients ； AdaBoost

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