本文選題：小群體 + 數(shù)據(jù)關(guān)聯(lián)�。� 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：小群體檢測與跟蹤是智能視頻監(jiān)控系統(tǒng)的關(guān)鍵技術(shù),也是異常事件檢測、行為理解、場景理解等更高層次的視覺任務(wù)的基礎(chǔ)。小群體指的是在接近的運(yùn)動(dòng)區(qū)域中,若干具有動(dòng)作一致性的人群。監(jiān)控視頻中的小群體不僅反映了社會(huì)行為與安全問題,具有廣泛的應(yīng)用,而且是計(jì)算機(jī)視覺領(lǐng)域非常具有挑戰(zhàn)性的問題。小群體的檢測和跟蹤依賴于對(duì)個(gè)體運(yùn)動(dòng)目標(biāo)的檢測與跟蹤,同時(shí)也依賴于群體特征的描述和群體獲取算法。這涉及到圖像處理、模式識(shí)別和機(jī)器學(xué)習(xí)等多個(gè)領(lǐng)域的知識(shí),因此具有較高的理論研究價(jià)值。近年來,許多群體分析的算法不斷的被提出,但是由于監(jiān)控視頻中人群運(yùn)動(dòng)的活動(dòng)分布狀態(tài)、小群體的結(jié)構(gòu)動(dòng)態(tài)變化頻繁、相互遮擋問題以及復(fù)雜背景問題等都給小群體分析帶來了不小的挑戰(zhàn)。為此,本文研究了一種基于自適應(yīng)聚類小群體檢測與跟蹤的方法,該方法通過改進(jìn)多目標(biāo)跟蹤,優(yōu)化軌跡的相似度測量,并進(jìn)行自適應(yīng)聚類,來發(fā)現(xiàn)小群體并對(duì)小群體跟蹤。本文的主要工作分如下兩點(diǎn):(1)對(duì)運(yùn)動(dòng)目標(biāo)個(gè)體,提出了基于雙向速度的預(yù)判軌跡擬合的多跟蹤算法。它是針對(duì)監(jiān)控視頻中人群可能出現(xiàn)相互遮擋、復(fù)雜背景等問題提出的,目的是為后繼的基于自適應(yīng)聚類的小群體檢測算法提供更準(zhǔn)確的輸入。在公共數(shù)據(jù)集FM_dataset和SMOT數(shù)據(jù)集上實(shí)驗(yàn)結(jié)果證明,提出的基于雙向速度的預(yù)判軌跡擬合的方法能夠在目標(biāo)短時(shí)遮擋時(shí)恢復(fù)目標(biāo)的時(shí)空運(yùn)動(dòng)軌跡,大大提高了對(duì)多目標(biāo)跟蹤的準(zhǔn)確性。(2)提出了基于自適應(yīng)聚類算法的小群體檢測與跟蹤算法。它是根據(jù)多目標(biāo)檢測與跟蹤獲得的時(shí)空軌跡,通過進(jìn)行軌跡相似度的度量,對(duì)度量場景的直方圖統(tǒng)計(jì)分布,自適應(yīng)的確定分割小群體的閾值,進(jìn)而通過聚類算法自適應(yīng)的判斷小群體的分裂與合并。最后基于正確率、缺失率、誤判率等評(píng)價(jià)指標(biāo),在FM_dataset數(shù)據(jù)集上驗(yàn)證了本文提出方法的魯棒性。
[Abstract]:Small group detection and tracking is the key technology of intelligent video surveillance system. It is also the basis of higher level visual tasks such as abnormal event detection, behavior understanding, scene understanding and so on. A small group refers to a group of people who have a consistent movement in a close movement area. The small groups in surveillance video not only reflect the social behavior and security problems, but also are very challenging in the field of computer vision. The detection and tracking of small groups depend on the detection and tracking of individual moving targets, as well as on the description of population characteristics and the algorithm of group acquisition. It involves many fields such as image processing, pattern recognition and machine learning, so it has high theoretical value. In recent years, many group analysis algorithms have been proposed, but because of the distribution of crowd movement in the surveillance video, the structure of small groups changes frequently. Mutual occlusion and complex background problems all pose a great challenge to the analysis of small groups. In this paper, a method based on adaptive clustering for small population detection and tracking is studied. This method can find small groups and track small groups by improving multi-target tracking, optimizing similarity measurement of trajectory, and carrying out adaptive clustering. The main work of this paper is as follows: 1) A multi-tracking algorithm based on bidirectional velocity predictive trajectory fitting is proposed for moving target individuals. It aims to provide more accurate input for the following small group detection algorithm based on adaptive clustering. The experimental results on the common data sets FM_dataset and SMOT show that the proposed method based on bidirectional velocity predictive trajectory fitting can restore the temporal and spatial trajectory of the target in a short period of time occlusion. The accuracy of multi-target tracking is greatly improved. (2) A small group detection and tracking algorithm based on adaptive clustering algorithm is proposed. It is based on multi-target detection and tracking of the space-time trajectory, through the measurement of trajectory similarity, the histogram statistical distribution of the measurement scene, adaptive determination of the threshold for segmentation of small populations. Then the clustering algorithm adaptively determines the split and merge of small population. Finally, the robustness of the proposed method is verified on the FM_dataset dataset based on the correct rate, missing rate, misjudgment rate and so on.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TN948.6

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本文編號(hào)：1901816