本文關(guān)鍵詞：智能視頻監(jiān)控中行人的檢測與跟蹤方法研究　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 視頻監(jiān)控 遷移學(xué)習(xí) 稀疏表達 行人檢測 粒子濾波 行人跟蹤

【摘要】：智能視頻監(jiān)控在獲取監(jiān)控視頻數(shù)據(jù)基礎(chǔ)上,對場景中的目標(biāo)如車輛,人進行檢測,檢測的方法是利用目標(biāo)的一些運動特征或者外觀特征如顏色,紋理等結(jié)合檢測窗口,檢測窗口可以是基于感興趣區(qū)域或者顯著性區(qū)域,也可以利用滑動窗口遍歷。在檢測基礎(chǔ)上可以進一步跟蹤目標(biāo),獲取目標(biāo)在一段視頻序列內(nèi)的軌跡。目標(biāo)的檢測和跟蹤是下一步目標(biāo)動作識別,行為分析的基礎(chǔ),但是由于場景中的背景時刻在變化且有光照,噪聲的影響再加上目標(biāo)之間相互遮擋給目標(biāo)的檢測和跟蹤增加了難度。同時以往的行人檢測方法主要是在公有數(shù)據(jù)集的基礎(chǔ)上提取特征然后利用分類器模型訓(xùn)練,所得行人檢測器在原始數(shù)據(jù)集上往往能得到較高的準(zhǔn)確率。但是一旦應(yīng)用到其他場景中,檢測率將大大下降。本文提出了一種基于遷移學(xué)習(xí)和稀疏編碼的行人檢測框架,該框架可以將在原訓(xùn)練集上訓(xùn)練好的行人檢測器遷移到新場景中,該框架中首先將原始檢測器應(yīng)用到目標(biāo)場景中獲得初始檢測結(jié)果,然后利用一些線索過濾出那些被檢測器正確分類的樣本作為目標(biāo)模板,然后利用稀疏編碼刻畫目標(biāo)模板和目標(biāo)樣本之間的相似性并且加權(quán)目標(biāo)樣本。同時,利用顯著性檢測方法檢測目標(biāo)模板和原訓(xùn)練集中行人樣本的顯著性區(qū)域,并利用稀疏編碼加權(quán)原訓(xùn)練集中的樣本,最后利用支持向量機訓(xùn)練所有帶權(quán)值樣本得到目標(biāo)場景下的行人檢測器�；谶w移學(xué)習(xí)所得的該檢測器在特定新場景中檢測率比原始檢測器提高了近30%。本文同時還提出一種基于粒子濾波行人跟蹤的框架,詳盡的闡述了粒子濾波框架的原理,即如何從貝葉斯理論和蒙特卡洛過渡到粒子濾波方法理論。基于行人顏色特征和粒子濾波框架下結(jié)合上一步檢測方法的驗證實現(xiàn)新場景下行人的跟蹤且實驗證明該跟蹤方法較傳統(tǒng)的跟蹤方法具有更好的魯棒性。
[Abstract]:Intelligent video surveillance is based on the acquisition of video data, the scene of the targets such as vehicles, people to detect, the method of detection is to use some of the moving features of the target or appearance features such as color. Texture and other combined detection window, detection window can be based on the region of interest or significant region, or can be traversed by sliding window, on the basis of detection can be further tracking the target. Target detection and tracking is the basis of the next target action recognition and behavior analysis, but because the background of the scene is changing and there is light. The influence of noise and the mutual occlusion between targets increase the difficulty of target detection and tracking. Meanwhile, the previous pedestrian detection methods are mainly based on the common data set to extract features and then train by classifier model. . The obtained pedestrian detector can obtain high accuracy in the original data set, but once applied to other scenarios. The detection rate will be greatly reduced. This paper proposes a pedestrian detection framework based on migration learning and sparse coding, which can transfer the trained pedestrian detectors on the original training set to a new scene. In this framework, the original detector is first applied to the target scene to obtain the initial detection results, and then some clues are used to filter out the samples correctly classified by the detector as the target template. Then sparse coding is used to describe the similarity between target template and target sample and weighted target sample. At the same time, significance detection method is used to detect significant area between target template and pedestrian sample in the original training set. The sample of the original training set is weighted by sparse coding. Finally, support vector machine (SVM) is used to train all weighted samples to obtain pedestrian detectors in target scenarios. The detection rate of the detector based on migration learning is 30% higher than that of the original detector. At the same time, a framework of pedestrian tracking based on particle filter is proposed. The principle of particle filter framework is described in detail. That is, how to transition from Bayesian theory and Monte Carlo to particle filter theory. Based on pedestrian color characteristics and particle filter framework combined with the verification of the previous detection method to achieve the new scene downlink tracking and experimental results. This tracking method is more robust than the traditional tracking method.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN948.6

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本文編號：1413926