【摘要】：電梯轎廂的內(nèi)部空間狹小、密閉,是摔倒、侵害、搶劫和群體恐慌等事件的多發(fā)之地。通過視頻監(jiān)控,預(yù)防上述事件的發(fā)生對于維護(hù)民生安全具有非常重要的現(xiàn)實(shí)意義。本文對電梯轎廂內(nèi)的異常行為檢測問題進(jìn)行了較深入的研究,取得了一些有價值的研究成果。論文的主要工作包括:1.設(shè)計并實(shí)現(xiàn)了一個結(jié)構(gòu)上較為完整的實(shí)時的電梯轎廂內(nèi)異常行為的圖像檢測系統(tǒng)。首先用減背景的方法提取圖像中的人體目標(biāo),然后對檢測出的人體目標(biāo)區(qū)域的像素進(jìn)行統(tǒng)計分析以實(shí)現(xiàn)對電梯轎廂內(nèi)人數(shù)的估計。2.根據(jù)電梯轎廂內(nèi)人數(shù)的多寡,構(gòu)建了針對單人、兩人及多人異常行為檢測的模型和算法。當(dāng)電梯轎廂內(nèi)僅有一位乘客時,主要通過對目標(biāo)區(qū)域的投影處理檢測乘客是否有摔倒或蹲伏行為。當(dāng)電梯轎廂內(nèi)有兩位乘客時,主要檢測是否有暴力行為發(fā)生。通過計算乘客的運(yùn)動歷史圖,獲取運(yùn)動的時空表征,并據(jù)此構(gòu)造能量函數(shù),通過能量的多少判斷乘客之間是否有暴力行為發(fā)生。而當(dāng)電梯轎廂內(nèi)有多位乘客時,主要檢測是否有群體恐慌行為發(fā)生。首先計算乘客的運(yùn)動能量圖,然后將運(yùn)動能量圖分別向水平和豎直兩個方向投影,得到能量的空間分布直方圖,最后根據(jù)直方圖的空間分布特征,判定是否有群體性慌亂行為發(fā)生。3.為了實(shí)際驗(yàn)證本文所提出的電梯轎廂內(nèi)乘客異常行為檢測方法的有效性,采集和構(gòu)建了不同情況下乘客在電梯轎廂內(nèi)各種異常行為的數(shù)據(jù)集。該數(shù)據(jù)集包含單人視頻34個,兩人視頻28個,多人視頻26個。在該數(shù)據(jù)集上對本文所提出的上述算法的測試結(jié)果表明,本文所提出的異常行為檢測算法簡潔、實(shí)時、有效。
[Abstract]:The interior of the elevator car is small, closed and prone to falls, assaults, robberies and mass panic. Video surveillance to prevent the occurrence of these incidents for the maintenance of livelihood security has a very important practical significance. In this paper, the detection of abnormal behavior in elevator car is studied deeply, and some valuable research results are obtained. The main work of the thesis includes 1: 1. A complete real-time image detection system for abnormal behavior in elevator car is designed and implemented. Firstly, the human object in the image is extracted by subtraction method, and then the pixels of the detected human target area are statistically analyzed to estimate the number of people in the elevator car. According to the number of people in the elevator car, a model and algorithm for detecting abnormal behavior of single person, two person and many people is constructed. When there is only one passenger in the elevator car, it mainly detects whether the passenger falls or crouches by projecting the target area. When there are two passengers in the elevator car, violence is mainly detected. By calculating the movement history map of passengers, the space-time representation of motion is obtained, and the energy function is constructed accordingly, and the number of energy can be used to judge whether there is violence among passengers. When there are many passengers in the elevator car, the main detection is whether there is panic behavior. First, the motion energy map of passengers is calculated, then the motion energy map is projected horizontally and vertically, and the spatial distribution histogram of energy is obtained. Finally, according to the spatial distribution characteristics of the histogram, Determine whether group panic behavior occurred. 3. In order to verify the effectiveness of the method proposed in this paper for detecting the abnormal behavior of passengers in the elevator car, the data sets of passengers' abnormal behavior in the elevator car are collected and constructed. The dataset consists of 34 single-person videos, 28 two-person videos and 26 multi-person videos. The test results on the data set show that the algorithm proposed in this paper is simple, real-time and effective.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TU857;TP391.41

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本文編號：2189607