本文選題：公交車客流計(jì)數(shù) + Hi-3515��； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：公交車作為城市居民出行的主要交通工具,猶如大動(dòng)脈一般為城市的正常運(yùn)作提供動(dòng)力。然而由于經(jīng)濟(jì)的發(fā)展,城市交通情況變得越來越復(fù)雜,舊式公交系統(tǒng)的運(yùn)作模式存在明顯的不足,尤其體現(xiàn)在客流高峰期發(fā)車的時(shí)間,發(fā)車的次數(shù)等方面,而公交車又存在乘坐方式多,難以實(shí)名制的問題,所以統(tǒng)計(jì)公交車客流量并不容易。圖像從古至今就是人類學(xué)習(xí)知識(shí)、增長(zhǎng)認(rèn)知以及獲取信息的主要感知方式,隨著時(shí)代的發(fā)展,科技的不斷進(jìn)步,在計(jì)算機(jī)網(wǎng)絡(luò)日益發(fā)達(dá),圖像處理技術(shù)日趨完善的今天,利用計(jì)算機(jī)圖像處理技術(shù)來幫助人們完成各種復(fù)雜的信息整合和處理變得更加簡(jiǎn)單,利用圖像處理技術(shù)統(tǒng)計(jì)公交車的客流量便是其中之一。近年來,各個(gè)公共場(chǎng)合已經(jīng)出現(xiàn)了許多檢測(cè)客流的方法,其中基于紅外對(duì)射統(tǒng)計(jì)的方法就得到了廣泛認(rèn)可和應(yīng)用,但是紅外對(duì)射法對(duì)于公交車車內(nèi)環(huán)境比較復(fù)雜的現(xiàn)在不能很好地應(yīng)對(duì),如出現(xiàn)在檢測(cè)區(qū)反復(fù)經(jīng)過可能重復(fù)計(jì)數(shù)的情況,而且需要在車輛上外加紅外對(duì)射的設(shè)備;而基于重力感應(yīng)的統(tǒng)計(jì)技術(shù)也難以實(shí)現(xiàn)密集客流量統(tǒng)計(jì),而且檢測(cè)設(shè)備易損耗甚至損壞。目前,應(yīng)用基于視頻序列的圖像處理方法來進(jìn)行客流統(tǒng)計(jì)已經(jīng)逐漸成為主要的手段,利用幀差法,背景差分法,光流法等手段鎖定目標(biāo)區(qū)域來檢測(cè)運(yùn)動(dòng)目標(biāo),再通過對(duì)差分?jǐn)?shù)據(jù)的分析來統(tǒng)計(jì)客流量的多少,然而,幀差法等方法存在算法自身固有的缺陷,對(duì)于公交車復(fù)雜的客流情況并不能生搬硬套,直接應(yīng)用。本文針對(duì)幀間差分法的缺點(diǎn)提出了一種改進(jìn)算法,即將幀間差分后的差分值量化后順序轉(zhuǎn)換為一維特征數(shù)組,通過非線性濾波平滑特征,在排除噪聲干擾的同時(shí)保留有效突變點(diǎn);另外在視頻中指定檢測(cè)區(qū)域,對(duì)不同區(qū)域的特征數(shù)組使用歸一化處理與連續(xù)數(shù)值檢測(cè)等方法,計(jì)算出目標(biāo)通過檢測(cè)區(qū)域的數(shù)量,具體方法如下:首先利用背景差分生成感興趣區(qū)域,然后對(duì)感興趣區(qū)域的Y分量進(jìn)行差分,進(jìn)行中值濾波和自適應(yīng)閾值化,最后對(duì)波形檢測(cè)與判決。本文將此算法運(yùn)用于基于Linux系統(tǒng)的Hi-3515平臺(tái)上,搭建了一個(gè)客流統(tǒng)計(jì)系統(tǒng),并進(jìn)行實(shí)驗(yàn)驗(yàn)證,完成了區(qū)域密集客流流量計(jì)數(shù)系統(tǒng)的實(shí)現(xiàn)。實(shí)驗(yàn)表明此算法可顯著降低幀間差分的運(yùn)算量,提高了密集人群流量計(jì)數(shù)的準(zhǔn)確率。
[Abstract]:As the main means of transportation for urban residents, bus provides power for the normal operation of the city.However, due to the development of economy, the urban traffic situation becomes more and more complicated, and the operation mode of the old public transport system has obvious shortcomings, especially in the time of departure during the rush hour of passenger flow, the number of times of departure, and so on.But the bus has many riding ways, difficult to real-name system, so it is not easy to count the bus passenger flow.Image is the main way for human beings to learn knowledge, increase cognition and obtain information from ancient times. With the development of the times and the progress of science and technology, the computer network is increasingly developed and the image processing technology is becoming more and more perfect.The use of computer image processing technology to help people to complete a variety of complex information integration and processing become easier, the use of image processing technology to calculate the bus passenger flow is one of them.In recent years, there have been many methods for detecting passenger flow in various public places, among which the method based on infrared photogrammetry has been widely recognized and applied.However, the infrared shooting method is not able to deal with the complex environment in the bus, such as the repeated repeated counting in the detection area, and the need to add infrared shooting equipment to the vehicle.The statistical technique based on gravity induction is also difficult to realize the dense passenger flow statistics, and the equipment is easy to wear and even damage.At present, the method of image processing based on video sequence for passenger flow statistics has gradually become the main means, using frame difference method, background difference method, optical flow method and other means to lock the target area to detect moving targets.Then through the analysis of the differential data to count the number of passenger flow, however, the frame difference method and other methods have their own inherent defects, for the bus complex passenger flow situation can not be mechanically applied.In this paper, an improved algorithm is proposed to solve the shortcoming of the inter-frame difference method, which is to transform the difference between frames into a one-dimensional feature array after quantization, and to smooth the feature by nonlinear filtering.In addition, the detection region is designated in the video, and the number of target detection regions is calculated by using normalization processing and continuous numerical detection for the characteristic array of different regions.The specific methods are as follows: firstly, the region of interest is generated by background difference, then the Y component of the region of interest is differential, median filtering and adaptive thresholding are carried out, and finally the waveform is detected and determined.In this paper, the algorithm is applied to the Hi-3515 platform based on Linux system, a passenger flow statistics system is set up, and the experiment is carried out to verify the realization of the system of the area intensive passenger Flowmeter.Experiments show that the algorithm can significantly reduce the computation of the difference between frames and improve the accuracy of the Flowmeter.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495;TP391.41

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