本文關鍵詞：復雜交通視頻場景中的車輛軌跡提取及行為分析　出處：《長安大學》2016年博士論文　論文類型：學位論文

  更多相關文章： 車輛軌跡提取 行為分析 視頻檢測 局部特征 光流法 相似性度量 Dirichlet過程混合模型

【摘要】：基于視頻的車輛運動軌跡提取及行為分析作為一個多學科交叉融合形成的研究領域,涵蓋了數(shù)字圖像處理技術、人工智能以及模式識別等多學科知識。然而,由于該領域研究對象復雜,涉及學科眾多,目前仍有很多難點問題亟待解決。復雜交通場景中運動車輛檢測、跟蹤和行為識別一直是該領域研究的熱點和難點,許多方法和技術還不夠成熟和完善。本文圍繞基于視頻的車輛軌跡提取與行為分析中的目標車輛檢測、運動車輛跟蹤、車輛軌跡相似性度量和軌跡聚類等關鍵問題進行了深入研究,取得了以下主要研究成果:1)針對復雜交通場景下的車輛目標檢測,本文提出一種基于車輛對稱特征和陰影特征的車輛目標檢測方法。該方法在SURF特征提取算法的基礎上,利用水平鏡像矩陣構造新的SURF特征描述算子。由于視覺上具有對稱特性的特征點處于不同尺度時,其匹配誤差會比較大。因此,本文從減少Haar特征累加次數(shù)和降低尺度對特征點表示的影響兩方面入手,對S-SURF算法進行改進和優(yōu)化。然后采用優(yōu)化的S-SURF算法對車輛的對稱特征進行提取,并利用車輛對稱特性對車輛的中心位置進行定位,最后,根據(jù)車輛底部陰影特征對車輛目標進行識別和區(qū)域定位。實驗結果表明,該方法利用局部不變特征集合來描述車輛目標,有效地避免了復雜場景下的目標分割難題,同時簡化了局部特征檢測方法中的聚類問題,復雜度較低,且具有較高的準確性。2)運動車輛的可靠穩(wěn)定跟蹤是車輛軌跡提取的關鍵。本文提出一種融合特征匹配和光流法的車輛目標跟蹤方法,該方法在基于雙向可逆性約束的KLT算法的基礎上,構造新的偏移量估算方法,對穩(wěn)定性較差的特征點進行剔除,提高了特征點跟蹤的可靠性和穩(wěn)定性。同時,采用SURF特征匹配算法作為補償機制對目標特征點集進行更新和校正。最后,結合初始幀中特征點之間相對位置和相對角度的關系,確定當前幀中目標的尺度變化和旋轉變化,并采用層次聚類的方法,對特征點進行聚類,以此刪除異常特征點,從而確定當前幀中的目標區(qū)域。該算法將兩個匹配策略相結合,既提高了跟蹤算法的穩(wěn)定性,也很好地解決了目標在被跟蹤過程中發(fā)生的形變、部分遮擋等問題,對目標的尺度和旋轉變化也具有較強的魯棒性。3)運動軌跡的相似性度量是軌跡聚類過程中的一個核心問題,由于車輛軌跡的復雜性和多樣性,現(xiàn)有度量方法都有其局限性。本文提出一種融合多特征和編輯距離的軌跡相似性度量方法。該方法在EDR編輯距離的基礎上,結合軌跡點的速度和方向特征,對軌跡進行分段處理,并給具有不同特征意義的分段賦予不同的編輯操作代價值。最后,對基于分段表示的IEDR算法進行了進一步的定義和分析。該算法保留了EDR算法的允許時間伸縮、抗噪性等優(yōu)點的同時,將軌跡點的位置、速度和方向特征合理地融入到車輛運動軌跡的相似性度量中,進一步提高了軌跡相似性度量的準確性和魯棒性。4)車輛行為模式學習的目的是提取出具體交通場景的常態(tài)運動模式,從而為車輛異常行為識別研究提供前提條件。本文提出一個基于增量式DPMM的貝葉斯最大后驗概率估計方法的軌跡聚類模型。該方法采用DFT系數(shù)作為軌跡的特征表示方法,提出一種基于DPMM的軌跡聚類方法,并在此基礎上,對Gibbs抽樣過程進行改進,以已分類軌跡作為先驗知識,對新增軌跡類別進行劃分。同時,在分類過程中,學習軌跡的常態(tài)運動模式,通過運動模式和方向模式匹配策略,對車輛異常行為進行判別。該算法不需要訓練樣本,而且隨著新增軌跡的到來而變化,聚類模型能夠實現(xiàn)自適應變化及模型參數(shù)學習和分類數(shù)目自動更新的任務,很好地解決了由于交通異常行為的不可預知、不常發(fā)生性引起的數(shù)據(jù)稀疏情況下的模型訓練困難問題。同時,利用已有聚類結果,將每次新增軌跡劃分到已有類別或新類中,不需要每次對所有軌跡進行重新聚類,聚類效率大大提高。
[Abstract]:Video based vehicle motion trajectory extraction and behavior analysis is a research field formed by multidisciplinary fusion, which covers multidisciplinary knowledge such as digital image processing technology, AI and pattern recognition. However, because of the complexity of the research object in this field and many subjects, there are still many difficult problems to be solved. Moving vehicle detection, tracking and behavior recognition in complex traffic scenes has been a hot and difficult topic in the field. Many methods and technologies are not mature enough. This paper focuses on the analysis of the target vehicle vehicle trajectory extraction and behavior in video detection, vehicle tracking, vehicle trajectory similarity measure and the key problem of trajectory clustering based on in-depth research, the main results are as follows: 1) for vehicle target detection under complex traffic scene, this paper proposes a vehicle detection method for target the vehicle features and shadow features based on symmetry. On the basis of the SURF feature extraction algorithm, this method constructs a new SURF feature description operator by using the horizontal mirror matrix. Because the feature points of the visual symmetry are in different scales, the matching error will be larger. Therefore, this paper improves and optimizes the S-SURF algorithm from two aspects: reducing the number of Haar feature accumulating times and reducing the impact of the scale on the representation of the feature points. Then, the optimized S-SURF algorithm is used to extract the symmetrical characteristics of the vehicle, and the vehicle's central location is located by the symmetry characteristics of the vehicle. Finally, the vehicle's target is identified and located according to the shadow feature of the vehicle bottom. The experimental results show that the proposed method uses local invariant feature set to describe vehicle targets, effectively avoids the problem of target segmentation in complex scenes, and simplifies the clustering problem in local feature detection, with low complexity and high accuracy. 2) the reliable and stable tracking of the moving vehicles is the key to the vehicle trajectory extraction. The vehicle target tracking method this paper proposes a fusion feature matching and optical flow method, the method based on KLT algorithm of bidirectional reversible constraints on the structure of the new method to estimate the offset, poor stability of feature points are removed, improves the feature tracking reliability and stability. At the same time, the SURF feature matching algorithm is used as compensation mechanism to update and correct the target set of feature points. Finally, combined with the relationship between the feature points in the initial frame relative position and angle of the scale change to determine the target in the current frame and rotation, and the method of hierarchical clustering, clustering of feature points, in order to remove abnormal points, so as to determine the area of the object in the current frame. The algorithm combines two matching strategies, which not only improves the stability of tracking algorithm, but also solves the problems of deformation and partial occlusion during target tracking. It also has strong robustness to the scale and rotation of targets. 3) the similarity measurement of trajectory is a core problem in trajectory clustering. Due to the complexity and diversity of vehicle trajectories, existing metric methods have their limitations. In this paper, a method of trajectory similarity measurement is proposed, which combines multiple features and edit distance. Based on the edit distance of EDR, combined with the speed and direction characteristics of track points, the method segmented the trajectories and assigned different editing operations to different feature segments. Finally, the IEDR algorithm based on piecewise representation is further defined and analyzed. The algorithm preserves the advantages of EDR algorithm such as the allowed time expansion and noise immunity. Meanwhile, it integrates the location, velocity and direction characteristics of trajectories reasonably into the similarity measurement of vehicle trajectories, which further improves the accuracy and robustness of trajectory similarity measurement. 4) the purpose of vehicle behavior model learning is to extract the normal motion pattern of specific traffic scene, thus providing the precondition for the research of vehicle abnormal behavior recognition. This paper presents a trajectory clustering model based on an incremental DPMM based Bayesian maximum a posteriori probability estimation method. In this method, the DFT coefficient is used as the characteristic expression method of trajectory. A trajectory clustering method based on DPMM is proposed. Based on that, the Gibbs sampling process is improved, and the classified track is used as a priori knowledge to classify the new trajectory categories. At the same time, in the classification process, the normal motion model of learning trajectory is used to discriminate the abnormal behavior of the vehicle through the motion pattern and the direction pattern matching strategy. The algorithm does not need training samples, and changes with the arrival of new track, clustering model can realize adaptive and parameter learning and classification number of automatic update tasks, is a good solution to the traffic abnormal behavior is unpredictable, infrequent training difficult model data sparseness problem caused by the situation. At the same time, we use existing clustering results to divide every new trajectory into existing categories or new classes, and do not need to re track all trajectories at any time, so the clustering efficiency is greatly improved.
【學位授予單位】：長安大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41

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