本文選題：公共自行車　切入點：視頻監(jiān)控　出處：《江南大學》2017年碩士論文　論文類型：學位論文

【摘要】：公共自行車作為一種低碳環(huán)保的交通工具,有效解決了最后一公里難題,在城市公共交通系統(tǒng)中發(fā)揮著不可替代的作用。但是目前社會上正在運營的是一車一樁式的有樁公共自行車系統(tǒng),不僅土地資源浪費嚴重,而且建設和維護的成本較高。因此,無樁公共自行車是未來發(fā)展的一個趨勢。但是由于沒有固定樁位的限制,自行車的安全得不到保障,在停車區(qū)域內(nèi)容易發(fā)生一些異常事件,例如在未得到許可的情況下公共自行車被人移動、目標在停車區(qū)域內(nèi)徘徊等,而傳統(tǒng)的視頻監(jiān)控系統(tǒng)一般不能起到異常事件報警作用。為此,根據(jù)無錫某公共自行車公司的開發(fā)需求,為實現(xiàn)公共自行車停車區(qū)域內(nèi)異常事件的檢測,開展了公共自行車視頻監(jiān)控中異常事件檢測技術研究,并設計了嵌入式應用驗證平臺對研究結果進行了測試和分析,為公司基于機器視覺的公共自行車系統(tǒng)開發(fā)提供了技術支撐。針對視頻監(jiān)控中公共自行車無故被人移動或被盜,以及目標在停車區(qū)域內(nèi)徘徊這兩個異常事件檢測技術開展了研究,提出了這兩種異常事件的檢測算法。最后設計了嵌入式應用驗證平臺,并將這兩種算法移植到嵌入式應用驗證平臺上進行了測試和分析。論文的主要工作如下:(1)為了檢測出公共自行車無故被人移走的異常事件,針對戶外環(huán)境復雜動態(tài)程度較高,背景中存在大量噪聲的場景,提出了一種改進的視覺背景提取的運動目標檢測算法。針對ViBe(visual background extractor,視覺背景提取)算法在初始化背景模型時容易產(chǎn)生鬼影以及對復雜動態(tài)場景適應能力差的問題,通過前景點計數(shù)的方法抑制鬼影的產(chǎn)生并根據(jù)場景的復雜動態(tài)程度自適應調(diào)整閾值提高算法的適應能力。實驗表明,與ViBe算法相比改進算法目標檢測的準確率提高了30%以上。(2)為了有效檢測出監(jiān)控區(qū)域內(nèi)發(fā)生的異常徘徊事件,在本文提出的運動目標檢測算法的基礎上,提出了一種基于運動目標軌跡分析的徘徊檢測算法。通過提取目標的運動軌跡,并計算軌跡離散點的運動方向熵和運動軌跡的主方向角,結合行人的運動距離來判定行為是否是徘徊行為。與其他算法相比,提出的算法無需建立樣本庫,簡單、有效,能滿足實際應用要求。(3)為了對研究結果進行驗證,設計了嵌入式應用驗證平臺,主要包括硬件平臺的設計、嵌入式Linux操作系統(tǒng)的搭建以及相關軟件的設計。針對實際應用場景,基于該平臺對上文的研究結果進行了驗證,驗證結果表明,在復雜動態(tài)場景下能夠實現(xiàn)公共自行車視頻監(jiān)控中異常事件檢測,滿足實際應用的需求。
[Abstract]:As a low-carbon and environmentally friendly vehicle, public bicycle has effectively solved the last kilometer problem. It plays an irreplaceable role in the urban public transportation system. But at present, what is being operated in the society is a public bicycle system with piles, which is not only a serious waste of land resources, but also a high cost of construction and maintenance. Pile-free public bicycle is a trend of development in the future. However, due to the lack of fixed pile position, the safety of bicycle is not guaranteed, so it is easy to occur some abnormal events in the parking area. For example, the public bicycle is moved without permission, and the target hovers in the parking area, while the traditional video surveillance system does not usually serve as an alarm for abnormal events. According to the development demand of a public bicycle company in Wuxi, in order to detect abnormal events in public bicycle parking area, the research on detection technology of abnormal events in public bicycle video surveillance is carried out. The embedded application verification platform is designed to test and analyze the research results, which provides technical support for the development of public bicycle system based on machine vision. And two abnormal event detection techniques are studied, and the detection algorithms of these two abnormal events are proposed. Finally, an embedded application verification platform is designed. The two algorithms are transplanted to the embedded application verification platform for test and analysis. The main work of this paper is as follows: 1) in order to detect the abnormal events of the public bicycle being removed by people without reason, the outdoor environment is more complex and dynamic. Where there's a lot of noise in the background, An improved algorithm for moving target detection based on visual background extraction (ViBe(visual background extractor) is proposed. When initializing the background model, the algorithm is prone to produce ghost images and poor adaptability to complex dynamic scenes. The method of spot counting is used to suppress the ghost and adjust the threshold to improve the adaptive ability of the algorithm according to the complex dynamic degree of the scene. Compared with the ViBe algorithm, the accuracy of the improved algorithm is improved by more than 30%.) in order to detect the abnormal hovering events in the monitoring area effectively, the proposed algorithm is based on the moving target detection algorithm proposed in this paper. A hovering detection algorithm based on trajectory analysis of moving object is proposed. The entropy of motion direction and the main direction angle of trajectory are calculated by extracting the moving trajectory of the target, and calculating the entropy of the motion direction of the discrete point of the trajectory, and the main direction angle of the trajectory. Compared with other algorithms, the proposed algorithm does not need to establish a sample base, simple, effective, can meet the practical application requirements. The embedded application verification platform is designed, including the design of hardware platform, the construction of embedded Linux operating system and the design of related software. The verification results show that the detection of abnormal events in public bicycle video surveillance can be realized in the complex dynamic scene and meet the practical requirements.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN948.6

【相似文獻】

相關期刊論文 前6條

1 申喜芹;郭瓊瓊;張宸豪;彭宏玉;;高速公路異常事件智能響應系統(tǒng)設計[J];中國交通信息化;2014年01期

2 吳新宇;郭會文;李楠楠;王歡;陳彥倫;;基于視頻的人群異常事件檢測綜述[J];電子測量與儀器學報;2014年06期

3 薄璐;薄瑋;;智能交通異常事件檢測系統(tǒng)的分析與設計[J];微型電腦應用;2014年06期

4 劉振娟;周培建;李宏光;林曉琳;;基于一類賦時模糊Petri網(wǎng)的化工過程異常事件監(jiān)控[J];化工學報;2008年07期

5 李兵;李秀;劉文煌;鄭啟鵬;;基于異常感知的動態(tài)維修服務管理[J];計算機集成制造系統(tǒng);2006年08期

6 鄭誠;舒堅;;多尺度時間序列異常事件檢測[J];計算機工程與應用;2006年31期

相關會議論文 前1條

1 申麗萍;姜玉杰;隋榮昌;紀守新;王建生;;開展QC小組活動,縮短隧道異常事件處理時間[A];公路交通與建設論壇（2011）[C];2003年

相關碩士學位論文 前10條

1 徐峗;公共自行車視頻監(jiān)控中異常事件檢測技術研究[D];江南大學;2017年

2 肖華強;基于達芬奇平臺的視頻異常事件檢測算法研究與實現(xiàn)[D];電子科技大學;2015年

3 余昊;基于底層特征的視頻異常事件檢測算法研究與實現(xiàn)[D];上海交通大學;2015年

4 馬金秀;基于視頻的無監(jiān)督異常事件檢測[D];電子科技大學;2016年

5 彭芊;基于在線自適應字典學習的視頻異常事件檢測技術研究[D];哈爾濱工業(yè)大學;2015年

6 周靈娟;視頻中異常事件檢測與特征稀疏表示研究[D];杭州電子科技大學;2016年

7 洪嘉鳴;基于數(shù)據(jù)的城市供水管網(wǎng)建模分析和異常事件偵測[D];杭州電子科技大學;2016年

8 劉威;視頻異常事件檢測算法設計與實現(xiàn)[D];江西財經(jīng)大學;2016年

9 羅丹;臨床護理人員對臨床異常事件報告現(xiàn)狀及對策研究[D];第二軍醫(yī)大學;2010年

10 彭寅艷;高速公路隧道條件下異常事件影響的模擬研究[D];重慶大學;2015年

，

本文編號：1609931