本文選題：運動檢測　切入點：目標跟蹤　出處：《廣西師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著電子信息產(chǎn)業(yè)制造技術的提高、硬件成本的降低,給計算機視覺技術帶來了飛速發(fā)展的機會,加上社會各行業(yè)對智能視頻的需求與日俱增,使得智能視覺成為一個理論和技術應用上熱門的研究領域,基于視頻的智能人數(shù)統(tǒng)計成為了該領域的熱門研究方向之一。本文設計的基于視頻的智能人數(shù)統(tǒng)計系統(tǒng)主要是針對于商場的人數(shù)統(tǒng)計,通過統(tǒng)計主要通道客流狀態(tài),從而進行店面的合理分布;統(tǒng)計各個區(qū)域的吸引率和繁忙度;有效評估所舉行的營銷和促銷投資的回報;顯示當前客流狀態(tài)和變化趨勢,安全保衛(wèi)部門可以對流量較大的區(qū)域采取預防突發(fā)事件的措施,并可實時觀察當前的實際人數(shù)及圖像等,比傳統(tǒng)的監(jiān)控系統(tǒng)更加智能化。本文從硬件和軟件部分進行設計,硬件方面與傳統(tǒng)方案區(qū)別不大,只是比一般的系統(tǒng)增加了基于App的客戶端,所以不做詳細介紹,系統(tǒng)實現(xiàn)的重點與難點在于對運動人體目標檢測技術、頭部識別的機器學習方法和移動目標跟蹤計數(shù)等,所以從以下幾個方面展開研究:首先,分析人數(shù)統(tǒng)計系統(tǒng)使用的環(huán)境是商場的門口等人流密集的地方,通過方案比較,我們選取采用Rossi等提出的攝像機垂直架設拍照方法,采取檢測人體頭部區(qū)域。其優(yōu)點是當行人靠近或肢體之間相互接觸發(fā)生遮擋時,依然能夠提取行人較為完整的頭部信息,盡可能地減少由于遮擋引起的漏判。然后,使用幀間差分法從視頻幀中提取運動目標,然后再以運動目標的外接矩形框作為后續(xù)人頭檢測的檢測區(qū)域。研究表明行人頭部檢測采用基于HOG特征提取、線性支持向量機作為分類器的檢測方法,是目前行人檢測中綜合性能較好的。接下來對視頻場景中的行人進行跟蹤計數(shù)。研究了經(jīng)典的HS光流法和LK光流法。其中LK光流計算方法因為靈活性高、計算量相對較小更適合應用在目標跟蹤中。對于空間運動位移較大的光流計算,將圖像進行金字塔分解來提高光流矢量求解的精確度。在計數(shù)時,對場景設定感興趣區(qū)域,并只對經(jīng)過感興趣區(qū)域的行人進行計數(shù),并可以準確的判斷進出方向�？梢愿鶕�(jù)實際的需要情況,隨意設定感興趣區(qū)域,從而提高了系統(tǒng)的實用性。最后,對于APP的開發(fā),本文主要介紹iOS操作系統(tǒng)的APP客戶端的開發(fā),采用的流程為:服務器端把檢測到人數(shù)變化的圖片保存為jpg格式文件,并存儲在服務器,再向遠程控制終端發(fā)送通知,控制終端解析推送通知,通過協(xié)議請求服務器中的圖片以獲得人數(shù)統(tǒng)計結果的實時圖片。本文是以運動目標前景檢測、基于機器學習的頭部識別以及目標跟蹤等技術在人數(shù)統(tǒng)計系統(tǒng)中實現(xiàn)了具體的應用案例。并且能夠成功地在iOS手機客戶端接收到人數(shù)變化的通知,得到人數(shù)變化時的圖片。為了驗證所用到的算法在本文提出的硬件配置要求不高的系統(tǒng)中的有效性、實時性及可靠性,采取了對大量不同場景下及不同人數(shù)的條件下的視頻進行了測試,測試結果表明系統(tǒng)對于人數(shù)統(tǒng)計能夠準確、有效的檢測視頻當中的行人頭部,并在跟蹤計數(shù)時具有較好的實用效果,達到預期設計目的。
[Abstract]:In recent years, along with the electronic information industry of manufacturing technology, reduce the cost of hardware, has brought great opportunities for the development of computer vision technology, with all sectors of society, demand for intelligent video makes intelligent vision become grow with each passing day, a theory and technology applied on the hot research field, the number of intelligent video based on statistics has become one of the most popular research direction in this field. The design of the intelligent video system based on the number of statistics is mainly based on the number of shopping malls statistics, through the statistics of the main channel flow state, so as to store the reasonable distribution; statistics in various regions of the attractive rate and busy degree; effective evaluation of a marketing and promotion investment return; display the current status and trend of the passenger flow, the security departments can take emergency prevention measures for regional heavy traffic, and real-time observation The actual number and image, more intelligent than the traditional monitoring system. This paper designed from hardware and software, hardware and the traditional scheme is very different, than the average increase of App system based on client, so the details do not system, emphases and difficulties of the realization of human motion target detection the head of the recognition technology, machine learning method and moving target tracking and counting, so from the following several aspects: first, analysis of the use of statistical system environment is the mall entrance and other populated areas, through the plan comparison, we selected by Rossi's camera is vertically erected photographing method detected by human head region the utility model has the advantages of contact with each other. When the occlusion occurred between pedestrians or near the limb, still be able to complete the extraction of pedestrian head information, as far as possible To reduce the occlusion caused by the leakage judgment. Then, using the frame difference method to extract moving objects from video frames, then the moving target rectangle as the detection area following head detection. According to the research on pedestrian head detection using HOG based feature extraction, linear support vector machine classifier as detection method at present, pedestrian detection is a good comprehensive performance. The next track count of the pedestrian in the video scene. The classic HS LK optical flow method and optical flow method. The LK optical flow calculation method for high flexibility, less calculation is more suitable for application in target tracking. The space motion of large displacement optical flow calculation. The image of the Pyramid decomposition to improve the accuracy of optical flow vector solution. In the count, area of interest to the scene, and only to the region of interest for pedestrians The number, and can accurately determine the direction of import. According to the actual situation, arbitrarily set the region of interest, so as to improve the practicality of the system. Finally, for the development of APP, this paper mainly introduces the development of iOS operating system APP client, the server process is: to detect the number of pictures saved as JPG files, and stored in the server, and then sent to the remote control terminal, the control terminal of push notifications, real-time image server in the picture by protocol request to obtain statistical results. This paper is based on the number of motion object detection, machine learning head recognition and target tracking technology to achieve the application the specific cases in the statistical system based on. And successfully received at the mobile phone iOS client to change the number of notification, the picture changes. In order to get the number of inspection The effectiveness of the system hardware configuration requirements proposed by the algorithm used in this paper is not high in the real time and reliability, take on a large number of different scenarios and different number of video conditions were tested, the test results show that the system can accurately for the number of statistics, the effective detection of pedestrian head video, and has a better practical effect in the tracking and counting, achieve the expected design objective.

【學位授予單位】：廣西師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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