本文選題：摔倒檢測 + 滑動窗口��； 參考：《遼寧大學》2017年碩士論文

【摘要】：我國經(jīng)濟社會不斷發(fā)展,人口老齡化愈加嚴重,對自動化摔倒檢測有著迫切的需求。摔倒檢測方法主要可以分為兩個流派:第一種是基于物理傳感器件的可穿戴式設備檢測方法;第二種是近年來新興的基于視頻圖像處理的方法�？纱┐髟O備雖然可以精確的獲得人體相應關節(jié)的運動參數(shù),但由于其產(chǎn)生了穿戴負擔,所以對于老年人并不是十分適用。圖像處理和模式識別近年來迅猛發(fā)展,將視頻處理技術應用于摔倒檢測的條件已經(jīng)成熟。基于視頻的檢測方法由于其對受眾的方便性,在未來會有廣闊的應用前景。目前很多基于機器視覺的檢測方法都遵循先定位后追蹤的方式來獲取人體運動情況,這種方式面臨四個突出的問題:1、定位跟蹤運算量龐大;2、只能通過獲得人體輪廓信息來推斷人體狀態(tài);3、定位視頻中摔倒起始位置困難;4、跟蹤過程容易跟錯目標。為了解決上述四個問題,本文采用了基于滑動窗口的視頻事件表示方法,該方法的突出優(yōu)點是將視頻內(nèi)容抽象成時空節(jié)點集合,由于時空節(jié)點是對視頻內(nèi)容的完整抽象,一方面可以將原先在原視頻上的定位跟蹤替換成對時空節(jié)點的搜索;另一方面可以做到基于內(nèi)容的搜索,而非通過人體輪廓信息推斷人體狀態(tài)。針對時空路徑搜索問題,我們將原先應用于視頻事件檢測的時空路徑搜索算法在摔倒檢測上進行了拓展應用,利用該算法可以在局部迭代過程中求全局最優(yōu)解和低時間復雜度的優(yōu)點解決了在未知起點情況下進行快速時空路徑搜索問題。對于在復雜場景檢測中出現(xiàn)的路徑偏差問題,經(jīng)研究發(fā)現(xiàn)主要是由于幀間節(jié)點在連接過程中缺乏預測指導所致。根據(jù)摔倒動作的特性,我們將摔倒動作分階段拆解,用馬爾科夫過程模型來表示整個動作過程,進而提出了能夠增強時空路徑搜索中幀間節(jié)點局部連接性的轉(zhuǎn)移概率改進方法。在幀間節(jié)點的連接過程中依據(jù)轉(zhuǎn)移概率對下一幀節(jié)點的選擇提供指導,解決了路徑搜索過程中幀間節(jié)點關聯(lián)性不強的問題。為了對算法的有效性進行驗證,我們制作了新的多場景摔倒數(shù)據(jù)集,并將上述算法在數(shù)據(jù)集上進行了驗證。實驗結(jié)果表明本文提出的改進方法可以有效的應用在數(shù)據(jù)集上,并且能獲得比較好的檢測效果。
[Abstract]:With the development of economy and society, the aging of population is becoming more and more serious.Fall detection methods can be divided into two main schools: the first is a wearable device detection method based on physical sensing devices; the second is a new method based on video image processing in recent years.Although wearable devices can accurately obtain the motion parameters of the corresponding joints of the human body, they are not very suitable for the elderly because of their wearable burden.With the rapid development of image processing and pattern recognition in recent years, the condition of applying video processing to fall detection is ripe.Because of its convenience to the audience, the video detection method will have a broad application prospect in the future.At present, many detection methods based on machine vision follow the method of locating first and then tracking to obtain human motion.In this way, there are four outstanding problems: 1, the operation of location tracking is huge, the human body status can only be inferred by obtaining human contour information, the position of falling down in the location video is difficult and the tracking process is easy to follow the wrong target.In order to solve the above four problems, this paper adopts the method of video event representation based on sliding window. The outstanding advantage of this method is that the video content is abstracted into a set of space-time nodes, because the spatio-temporal node is a complete abstraction of the video content.On the one hand, the original location tracking on the original video can be replaced with the search of the space-time node; on the other hand, the content-based search can be achieved, instead of inferring the human body state from the human contour information.In order to solve the problem of spatio-temporal path search, we extend the application of space-time path search algorithm used in video event detection in fall detection.The algorithm can solve the problem of fast spatio-temporal path search with unknown starting point by using the advantages of global optimal solution and low time complexity in the local iterative process.For the problem of path deviation in complex scene detection, it is found that it is mainly due to the lack of prediction guidance in the connection of inter-frame nodes.According to the characteristics of falling motion, we divide the falling action into stages and use Markov process model to represent the whole action process, and then we propose an improved method which can enhance the local connectivity of inter-frame nodes in space-time path search.The selection of the next frame nodes is guided by the transition probability in the connection process of inter-frame nodes, which solves the problem that the inter-frame nodes are not correlated strongly in the course of path search.In order to verify the validity of the algorithm, we made a new multi-scene fall dataset and verified the algorithm on the dataset.The experimental results show that the improved method proposed in this paper can be effectively applied to the data set and can obtain a better detection effect.
【學位授予單位】：遼寧大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP212.9

【參考文獻】

相關期刊論文 前1條

1 ;我國人口老齡化進入急速發(fā)展期[J];城市規(guī)劃通訊;2012年10期

，

本文編號：1758563