本文選題：智能交通 + 目標檢測　； 參考：《華南理工大學》2014年博士論文

【摘要】：視頻目標的檢測、識別是目前智能交通和計算機視覺領域中的一個重要研究方向。但是，由于檢測和識別環(huán)境下存在背景復雜、光照變化、目標遮擋等原因，導致該應用仍面臨著許多困難，檢測和識別的魯棒性及準確性都有待進一步提高。 本論文對視頻目標檢測和識別中的幾個關鍵問題進行了研究，主要包括：復雜場景下目標與背景、陰影的準確分割；對提取的前景目標準確分類；復雜背景下的目標識別。針對這些問題，本論文提出了相應的解決方法。具體工作如下： 1.提出了一種基于自適應模糊估計的背景建模方法。該方法從函數(shù)估計的角度對背景進行建模，并采用TSK模糊系統(tǒng)作為估計函數(shù)。為了訓練函數(shù)估計算子，分別使用粒子群優(yōu)化（PSO）算法和遞歸最小二乘估計（RLSE）算法來優(yōu)化模糊系統(tǒng)的前件參數(shù)和后件參數(shù)。為了有效估計背景，將前景像素看作背景像素的異常樣例，并提出了異常樣例的去除方法，然后用去除后的結果去訓練模糊估計算子。該方法在動態(tài)背景、光照變化、攝像機振動等環(huán)境下都具有較高的運行效率和檢測效果。 2.提出了一種基于模糊積分的運動陰影檢測方法。在提取前景區(qū)域的基礎上，選擇顏色和紋理作為陰影檢測的特征，并分別定義了這兩種特征的相似性和重要性測度函數(shù)，然后通過Choquet模糊積分將這兩種特征融合，實現(xiàn)陰影和前景目標的分類，最后通過后續(xù)處理，找到真正的陰影區(qū)域。 3.提出了一種基于JointBoost I2C距離度量的目標分類方法。針對經典I2C距離計算量大且易受噪聲干擾等不足，首先提出了一種原型特征集的生成方法，該集合中的樣本數(shù)量較少，但更具有代表性，計算測試圖像到該原型特征集的距離花費較少時間；然后借助JointBoost算法的思想，聯(lián)合多個I2C距離度量生成一個強分類器；最后還提出了一種將空間信息融合到強分類器的方法。實驗證明，該方法在前景目標和圖像分類實驗中，具有更高的分類性能。 4.提出了基于特征碼本樹和能量最小化的目標識別方法。該方法考慮了特征的空間位置信息和特征之間的空間關系，集成了目標檢測和目標識別。首先從目標圖像提取的大量特征中過濾掉噪聲特征；然后對單特征和空間上鄰近的串聯(lián)雙特征分別使用層次k均值聚類算法構建特征碼本樹，，利用樹模型可以實現(xiàn)特征快速定位和分類；最后建立一個能量函數(shù)來融合單、雙特征碼本樹的類別概率匹配結果，并通過在測試圖像中尋找滑動窗口所在區(qū)域的能量最小化來確定所屬類別目標的位置。 5.提出了基于優(yōu)化Hough森林代價損失的目標識別方法。首先在充分利用訓練圖像中對象位置是已知的基礎上,提出了改進的偏移量不確定性度量方法；其次借助Boosting算法的思想，學習圖片塊樣本和目標對象樣本的自適應權重分布，并分別優(yōu)化用于構造隨機樹和Hough森林的代價損失函數(shù)；最后根據(jù)圖片塊樣本的權重分布，提出了改進的類標志不確定性度量方法。基于Hough森林的代價損失函數(shù)，還提出了隨機樹權重的學習方法。
[Abstract]:The detection and recognition of video targets is an important research direction in the field of intelligent traffic and computer vision. However, because of the complicated background, illumination change and target occlusion in the detection and recognition environment, the application still faces many difficulties. The robustness and accuracy of detection and recognition need to be further improved.
In this paper, several key problems in video target detection and recognition are studied, including: the target and background of the complex scene, the accurate segmentation of the shadow, the accurate classification of the foreground object and the target recognition under the complex background.
1. a background modeling method based on adaptive fuzzy estimation is proposed. This method models the background from the angle of function estimation and uses the TSK fuzzy system as the estimation function. In order to train the function estimation operator, the particle swarm optimization (PSO) algorithm and the recursive least double multiplicative estimation (RLSE) algorithm are used to optimize the pre fuzzy system. In order to effectively estimate the background, the foreground pixels are considered as an abnormal example of the background pixels, and the removal method of the anomaly samples is proposed. Then the fuzzy estimation operator is trained by the removal results. The method has high efficiency and detection in the dynamic background, the illumination change, the camera vibration and so on. Effect.
2. a motion shadow detection method based on fuzzy integral is proposed. On the basis of extracting foreground region, color and texture are selected as the feature of shadow detection, and the similarity and importance measure function of the two features are defined respectively. Then the two features are fused by Choquet fuzzy integral to realize the shadow and foreground object. Classification, and finally through the subsequent processing, find the real shadow area.
3. a target classification method based on JointBoost I2C distance measurement is proposed. In view of the shortage of classical I2C distance computation and easy to be disturbed by noise interference, a new method of generating prototype feature sets is proposed. The number of samples in the set is less, but more representative, the distance cost of the test image to the prototype feature set is calculated. Less time; then the idea of JointBoost algorithm is used to combine multiple I2C distance metrics to generate a strong classifier. Finally, a method of fusion of spatial information to a strong classifier is proposed. Experiments show that the method has a higher classification performance in the foreground object and the image classification experiment.
4. a target recognition method based on characteristic codebook tree and energy minimization is proposed. This method takes into account the spatial location information of the feature and the spatial relationship between features, and integrates target detection and target recognition. First, the noise features are filtered out from the large number of features extracted from the target image, and then the single feature and the adjacent space in the space are connected in series. The double feature uses the hierarchical K mean clustering algorithm to construct the characteristic tree tree. The tree model can be used to locate and classify the features quickly. Finally, an energy function is established to fuse the probability matching results of the single, double feature codebook, and to find the energy minimization of the region in which the sliding window is located in the test image. The position of the category target.
5. the target recognition method based on optimized Hough forest cost loss is proposed. Firstly, based on the known location of the object in the training image, an improved measurement method of offset uncertainty is proposed. Secondly, the adaptive weight distribution of the sample of picture block and target object is learned with the help of the thought of the Boosting algorithm. The cost loss functions used to construct random trees and Hough forests are optimized respectively. Finally, based on the weight distribution of the block samples, an improved method for measuring the uncertainty of the class marks is proposed. Based on the cost loss function of the Hough forest, the learning method of the weight of the random tree is also proposed.
【學位授予單位】：華南理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：U495

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