本論文發展兩個新穎的在影像中自動化車輛偵測方法。我們主要是在發展強大的特徵擷取演算法 (描述車輛影像的區域特徵，像是高維度的特徵向量)，與機器學習方法 (學習一個小量且具有特色與鑑別力的特徵向量的集合)，這可以提供高正確率的車輛偵測。
第一個方法是基於主成份分析 (principal component analysis，PCA) 加上獨立成份分析 (independent component analysis，ICA) 統計逼近法，這方法是基於三個在車輛影像上有意義的子區域的區域特徵分別投影到相對應的特徵空間與獨立基底空間去分別產生 PCA 的權重向量與 ICA 的係數向量。利用 PCA 來模組化在特徵空間的低頻成份與 ICA 來模組化剩餘空間的高頻成份可以改善在偵測處理的容忍度，這有助於容忍車輛的光線變化與角度姿勢的變異程度。為了學習具有特色與鑑別力的 PCA+ICA 特徵向量，利用一個有權重的高斯混和模型 (它的參數和權重是藉由期望值最大化 (Expectation-Maximization, EM) 演算法來反覆估算) 來模組化所有訓練車輛影像子區域的PCA 權重向量與 ICA 係數向量。然後基於計算子區域有位置資訊的 PCA 權重向量與 ICA 係數向量的結合機率來執行一個可能性估算處理流程 (likelihood evaluation process)來偵測在影像中的車輛。
第二個方法是基於梯度角度統計圖 (histograms of oriented gradients, HOG)的串聯式Boosting 逼近法（它是自動地學習在車輛上足夠且有意義的區域特徵資訊來偵測在影像上的車輛）。為了克服車輛幾何和旋轉變異程度的影響，這方法自動地為每一個區塊性特徵指定一個或多個主要的角度，然後藉由使用矩形與環形種類的梯度角度統計圖 (對於各種光線與雜訊不敏感尤其是在戶外環境) 來編碼成 HOG 特徵向量。為了實現可靠的偵測結果，一個具有強大能力的特徵學習演算法，AdaBoost，被執行去自動地學習一個小量且具有鑑別力的 HOG 特徵向量 (具有角度資訊) 集合。更進一步藉由使用串聯式結構 (使用 AdaBoost 來估算它每一階層的所有弱分類器和相對應的權重)，整個計算時間明顯地減少且不會降低任何的偵測效能。
在兩個方法的實驗中證明了採用區域特徵而不是全域資訊可改善偵測處理的容忍度，這有助於容忍車輛的幾何變異與部份遮罩。實驗證明在偵測效能上，基於 HOG 串聯式 Boosting 逼近法是優於基於 PCA+ICA 統計逼近法。另外，在人體偵測主題應用上，藉由測試 INRIA+MIT 行人資料庫，採用基於 HOG 串聯式 Boosting 逼近法的偵測效能是優於目前最新進的人體偵測方法。

This thesis develops two novel approaches for automatic vehicle detection to detect vehicles in the images. We focus on developing robust feature extraction algorithm which encode local features of vehicle image as high-dimensional feature vectors and on machine learning method in which a small set of distinctive and discriminative local features are learned and support high accuracy vehicle detections.
The first approach is the PCA (principal component analysis) + ICA (independent component analysis) based statistical approach. This approach is based on local features located within three significant subregions of the vehicle image and each subregion is then projected onto its associated eigenspace and independent basis space in order to generate a PCA weight vector and an ICA coefficient vector, respectively. The use of PCA to model the low-frequency components of the eigenspace and ICA to model the high-frequency components of the residual space can improve the tolerance of the detection process toward variations in the illumination conditions and vehicle pose. For learning distinctive and discriminative PCA+ICA feature vectors, a weighted Gaussian mixture model (GMM), whose parameters and weights are estimated iteratively using an Expectation-Maximization (EM) algorithm, is employed to model the PCA weight vectors and ICA coefficient vectors of all the subregions in training vehicle images. A likelihood evaluation process is then performed based on the joint probability of the PCA weight vectors and the ICA coefficient vectors of the subregions with position information to detect vehicles in the images.
The second approach is the histograms of orientated gradients (HOG) based cascaded Boosting approach, which can automatically learn significant local feature information of vehicle to detect vehicles in the images. To overcome the effects on geometric and rotational variations, this approach automatically assigns one or several dominant orientations to each block-based feature encoded by using the rectangular- and the circular-type HOG, which are insensitive to various lightings and noises particularly at the outdoor environment. Then a powerful feature learning algorithm, AdaBoost, is performed to automatically learn a small set of discriminative HOG feature vectors with orientation information in order to achieve robust detection results. The overall computational time is reduced significantly without any performance loss by using the cascaded structure, whose weak classifiers and corresponding weights of each stage are estimated by using the AdaBoost approach.
In the two approaches, the experimental results have shown that the use of local features rather than global information can improve the tolerance of the detection process toward geometric variance and partial occlusion. The experimental results demonstrated that the vehicle detection performance of HOG-based cascaded Boosting approach is better than PCA+ICA-based statistical approach. In addition, in human detection topic, the HOG-based cascaded Boosting approach is evaluated by using the INRIA+MIT pedestrian database to obtain superior detection performance than the recent human detection approaches.

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