發(fā)布時間：2018-11-27 18:29

【摘要】：車輛軌跡識別與在線異常檢測是智能交通系統(tǒng)中的一個重要研究方向,它對現(xiàn)實生活中發(fā)生的交通事故及時有效地救援和后期處理具有很大的輔助作用;同時它能減少因為交通事故引起的交通延誤和二次事故;它還可以為城市交通監(jiān)控與安全管理提供重要的信息憑證。通常情況下,車輛軌跡識別與在線異常檢測是基于軌跡建模的方法進行的,而軌跡建模方法分為統(tǒng)計模型和運動模型兩種。無監(jiān)督學(xué)習(xí)是近幾年提出的一種新的軌跡建模方法,該方法能在訓(xùn)練數(shù)據(jù)中排除異常軌跡,并且在訓(xùn)練數(shù)據(jù)比較多的情況下有效地進行軌跡識別及異常檢測,但是對于正常收集的含有異常軌跡的初始軌跡集及含有少量軌跡的初始軌跡集,該方法的識別率及異常檢測精度比較低。針對這些問題,本文將增量式(incremental)EM算法應(yīng)用到無監(jiān)督軌跡建模上,提出一種基于批處理(batch-mode)模型初始化的增量式軌跡建模方法,并將其應(yīng)用到車輛軌跡識別與在線異常檢測上。首先采用改進的Hausdorff距離測量初始軌跡集中軌跡之間的相似度,再用譜聚類算法對初始軌跡集進行聚類,提取軌跡的分布模式。對于初始軌跡集中聚類完成的每一類樣本軌跡,利用多觀察序列訓(xùn)練方法建立每類軌跡的隱馬爾科夫模型,依此來得到初始軌跡模型庫。對于從視頻圖像中提取的新軌跡,使用最大后驗估計尋找新軌跡最可能的正常軌跡類,再用自動閾值法進行在線異常檢測,然后識別新軌跡所屬的軌跡模型,通過incremental EM算法更新新軌跡所屬類別的隱馬爾科夫模型參數(shù)。為了增加本文方法的適應(yīng)性,最后需要對模型結(jié)構(gòu)進行更新。應(yīng)用本文方法對戶外實際場景拍攝視頻進行測試,實驗結(jié)果表明,與經(jīng)典的無監(jiān)督批處理軌跡建模算法相比,本文方法即增量式軌跡建�？梢缘玫礁�加準(zhǔn)確的軌跡模型庫、更快的運算速度;該算法在初始軌跡集包含異常軌跡的情況下能大幅度提高軌跡識別率;在異常檢測方面得到了更高的檢測率和更低的虛警率,實現(xiàn)了在線異常檢測,具有對初始軌跡集不敏感的特點。
[Abstract]:Vehicle trajectory identification and on-line anomaly detection are important research directions in intelligent transportation system. They can help traffic accidents in real life to rescue and deal with them in a timely and effective manner. At the same time, it can reduce traffic delays and secondary accidents caused by traffic accidents, and it can also provide important information evidence for urban traffic monitoring and safety management. Generally, vehicle trajectory identification and online anomaly detection are based on trajectory modeling, and trajectory modeling methods are divided into statistical model and motion model. Unsupervised learning is a new trajectory modeling method proposed in recent years. This method can eliminate abnormal trajectory in training data, and can effectively identify and detect the trajectory when there are more training data. However, the recognition rate and the detection accuracy of the method are low for the normal collection of initial trajectory sets with abnormal trajectories and the initial trajectory sets with a small number of tracks. To solve these problems, this paper applies incremental (incremental) EM algorithm to unsupervised trajectory modeling, and proposes an incremental trajectory modeling method based on batch processing (batch-mode) model initialization. It is applied to vehicle track recognition and online anomaly detection. Firstly, the improved Hausdorff distance is used to measure the similarity between the trajectories of the initial locus, and then the spectral clustering algorithm is used to cluster the initial locus to extract the distribution pattern of the locus. The hidden Markov model of each kind of trajectory is established by using the training method of multiple observation sequences for each kind of sample locus which is clustered by the initial locus, and the initial locus model library is obtained according to this model. For the new trajectory extracted from the video image, the maximum a posteriori estimation is used to find the most probable normal trajectory class, and then the automatic threshold method is used for on-line anomaly detection, and then the trajectory model to which the new trajectory belongs is identified. The hidden Markov model parameters of the new trajectory are updated by incremental EM algorithm. In order to increase the adaptability of this method, we need to update the model structure at last. The experiment results show that, compared with the classical unsupervised batch trajectory modeling algorithm, the incremental trajectory modeling method in this paper can get more accurate trajectory model base. Faster computing speed; The algorithm can greatly improve the trajectory recognition rate when the initial trajectory set contains abnormal trajectory. Higher detection rate and lower false alarm rate are obtained in anomaly detection, and on-line anomaly detection is realized, which is insensitive to the initial trajectory set.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;TP391.41

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