發(fā)布時間：2018-05-26 08:26

  本文選題：視頻監(jiān)控 + 目標檢測��； 參考：《北京交通大學》2017年碩士論文

【摘要】：隨著計算機技術(shù)的發(fā)展,智能視頻監(jiān)控技術(shù)在民用、軍事、航空、醫(yī)療和人機交互等眾多領(lǐng)域,都有廣泛的應(yīng)用,其重要性也越來越凸顯。運動目標的跟蹤是計算機視覺領(lǐng)域的一個重要分支,目前,基于視頻的目標跟蹤技術(shù)幫助人們解決了很多問題,但是仍然存在一些不足,提升視頻監(jiān)控的檢測效果和跟蹤效率,是一個很重要、很值得研究的課題。步態(tài)識別作為一種遠距離身份判別方式,具有非侵犯性、難以隱藏和難以偽裝的特性,鑒于此,將步態(tài)識別技術(shù)引入到目標跟蹤中,實現(xiàn)身份識別,然后進行定位跟蹤,在安防、刑事破案等領(lǐng)域具有重要應(yīng)用和意義。本文針對攝像機固定情況下錄制的視頻,首先對感興趣目標區(qū)域進行檢測,然后通過提取目標的步態(tài)特征進行身份識別,最后選定目標實現(xiàn)跟蹤。論文工作涉及到了目標的檢測、人體步態(tài)識別和目標跟蹤三個主要部分。論文的主要工作包括以下幾部分:(1)在目標檢測方面,研究了多種經(jīng)典的目標檢測算法,進行了實驗比較分析,綜合這幾種方法的優(yōu)缺點,在Vibe算法的基礎(chǔ)上進行改進,結(jié)合人體體型特征提出了一種PVibe(ProportionVibe)算法,對人體目標進行檢測。在簡單環(huán)境和復(fù)雜環(huán)境中進行人體目標檢測實驗對比,實驗結(jié)果表明,該算法與其他檢測算法相比,對于動態(tài)的環(huán)境具有較好的適應(yīng)能力,能夠很好的提取運動目標輪廓,區(qū)分人體目標與非人體目標,具有較好的檢測效果。(2)在步態(tài)識別方面,使用目標檢測分割算法從步態(tài)序列中得到步態(tài)剪影,提取人體輪廓,將質(zhì)心到輪廓邊緣各像素點之間的距離作為步態(tài)特征。利用BP神經(jīng)網(wǎng)絡(luò)分類算法對步態(tài)樣本中各序列的特征數(shù)據(jù)進行訓(xùn)練識別,從人體步態(tài)的不同角度進行實驗對比,最高識別率可達到88.33%。該算法與常用的最近鄰分類識別算法相比,識別率明顯提高,證明了算法的高效性。(3)目標跟蹤方面,在對目標進行了步態(tài)識別的基礎(chǔ)上,選定目標,確定感興趣區(qū)域,并進行跟蹤。研究了幾種主流的跟蹤算法,本文在研究和分析了光流跟蹤算法的優(yōu)點和不足的基礎(chǔ)上,結(jié)合運動矢量估計提出一種金字塔LK-MVE(LK-Motionvectorestimation)算法,對人體目標進行跟蹤。該算法與主流的跟蹤算法進行實驗對比,在目標顏色相近和出現(xiàn)遮擋的情況下,取得了較好的跟蹤效果,而且在跟蹤速度上,改進后的算法有明顯的提高。
[Abstract]:With the development of computer technology, intelligent video surveillance technology has been widely used in many fields, such as civil, military, aviation, medical and human-computer interaction, and its importance is becoming more and more prominent. Moving target tracking is an important branch in the field of computer vision. At present, video based target tracking technology has helped people solve many problems, but there are still some shortcomings to improve the detection effect and tracking efficiency of video surveillance. Is a very important, very worthy of study of the subject. Gait recognition, as a long distance identification method, is noninvasive, difficult to hide and difficult to camouflage. In view of this, gait recognition technology is introduced to target tracking to realize identification, and then to locate and track. In the security, criminal detection and other areas of important application and significance. In this paper, firstly, the region of interest is detected, then the gait feature of the target is extracted for identification. Finally, the target is selected for tracking. The work of this paper involves three main parts: target detection, human gait recognition and target tracking. The main work of this paper includes the following parts: 1) in the aspect of target detection, we study various classical target detection algorithms, compare and analyze the experiments, synthesize the advantages and disadvantages of these methods, and improve them on the basis of Vibe algorithm. In this paper, a PVibe-ProportionVibe-based algorithm is proposed to detect human targets. The experiments of human body target detection in simple environment and complex environment show that the algorithm has better adaptability to dynamic environment than other detection algorithms, and can extract the contour of moving target well. In gait recognition, gait segmentation algorithm is used to get gait silhouette from gait sequence and extract human contour. The distance between the centroid and the pixels on the edge of the contour is taken as a gait feature. BP neural network classification algorithm is used to train and recognize the characteristic data of each sequence in gait samples. The highest recognition rate can reach 88.33 from different points of view of human gait. Compared with the nearest neighbor classification algorithm, the recognition rate of this algorithm is obviously improved. It is proved that the algorithm is efficient. On the basis of gait recognition, the target is selected and the region of interest is determined. And follow up. Based on the research and analysis of the advantages and disadvantages of optical flow tracking algorithm, a pyramid LK-MVEV LK-Motional moving to timing (LK-MVEV) algorithm is proposed in this paper to track human body targets. Compared with the mainstream tracking algorithm, the proposed algorithm achieves good tracking results in the case of similar color and occlusion, and the improved algorithm has obvious improvement in tracking speed.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1936533