【摘要】：當(dāng)今社會(huì),車(chē)輛迅速增長(zhǎng),人們對(duì)交通網(wǎng)絡(luò)的需求也越來(lái)越強(qiáng)烈,只是依靠加寬道路已經(jīng)不能解決問(wèn)題,這樣的情況下,如何對(duì)交通網(wǎng)絡(luò)更為高效地管理便成為關(guān)鍵,而此時(shí)智能交通系統(tǒng)的出現(xiàn)為這一問(wèn)題的解決提供了方向,對(duì)監(jiān)控視頻中交通信息的提取是這個(gè)領(lǐng)域的重點(diǎn)研究?jī)?nèi)容,它可以為做出交通決策提供有力支持,緩解交通壓力,對(duì)交通車(chē)輛進(jìn)行合理調(diào)度,使交通網(wǎng)絡(luò)得以高效地進(jìn)行運(yùn)作。首先,本文對(duì)獲取到的視頻圖像做一定的處理,使得圖像達(dá)到可以實(shí)現(xiàn)車(chē)輛檢測(cè)的要求,這些處理包括灰度的轉(zhuǎn)換,直方圖均的衡化,去噪,二值化,形態(tài)學(xué)操作等內(nèi)容,然后仿真得出結(jié)果,并進(jìn)行了對(duì)比,選出最優(yōu)的處理方法。然后,再檢測(cè)運(yùn)動(dòng)車(chē)輛,這是車(chē)輛跟蹤和交通信息提取的重要前提,通過(guò)對(duì)比各種方法,并且通過(guò)對(duì)比實(shí)驗(yàn)結(jié)果,選取了背景差分法,因?yàn)楸尘安罘址ǹ梢垣@取到完整的車(chē)輛,而且這種方法的計(jì)算量小,可以應(yīng)用于對(duì)實(shí)時(shí)性要求高的場(chǎng)景中。之后需要對(duì)背景建模,通過(guò)改進(jìn)常用的背景建模方法,使得靜止或運(yùn)行速度很慢的車(chē)輛不會(huì)被當(dāng)作背景,能快速準(zhǔn)確獲取到背景。針對(duì)車(chē)輛陰影的存在,改進(jìn)了常用的車(chē)輛陰影去除方法,這種方法相比于常用的陰影去除方法要更加有效,陰影去除完全而且不會(huì)丟失車(chē)輛信息。其次,本文實(shí)現(xiàn)了車(chē)輛的跟蹤,這對(duì)接下來(lái)的車(chē)輛信息提取有著很關(guān)鍵的作用。通過(guò)對(duì)比跟蹤方法,本文最終選取了Camshift算法來(lái)跟蹤車(chē)輛,并引入了卡爾曼濾波器預(yù)估車(chē)輛接下來(lái)的運(yùn)動(dòng)情況,減小了對(duì)車(chē)輛位置搜索的范圍,很大程度上加快了運(yùn)算速度,使得車(chē)輛跟蹤更為高效,并且由于Camshift算法的特性,車(chē)輛遮擋問(wèn)題也能在一定程度上得到解決。最后,本文對(duì)交通信息進(jìn)行了提取,其中包括對(duì)車(chē)速進(jìn)行測(cè)量,對(duì)車(chē)流量進(jìn)行獲取,檢測(cè)車(chē)輛是否違章停車(chē)以及車(chē)輛是否逆行,并且對(duì)這些要獲取的交通信息,實(shí)驗(yàn)得出結(jié)果,根據(jù)結(jié)果可以得出結(jié)論,這些方法的準(zhǔn)確性和速度都能滿足要求。在車(chē)速車(chē)輛測(cè)速中,改進(jìn)了常用的車(chē)輛測(cè)速方法,使得測(cè)速結(jié)果更加精確。在違章停車(chē)的檢測(cè)中,改進(jìn)了常用的違章停車(chē)檢測(cè)方法,這種方法的檢測(cè)速度快,適合在監(jiān)控視頻中這種對(duì)實(shí)行性要求高的場(chǎng)景下使用。
[Abstract]:In today's society, with the rapid growth of vehicles, people's demand for transportation network is becoming more and more intense. It is not possible to solve the problem only by widening the road. In this case, how to manage the transportation network more efficiently becomes the key. At this time, the emergence of intelligent transportation system provides a direction for the solution of this problem. The extraction of traffic information from surveillance video is the key research content in this field. It can provide strong support for making traffic decisions and relieve traffic pressure. The traffic network can be operated efficiently by reasonable dispatching of traffic vehicles. First of all, this paper does some processing to the obtained video image, which makes the image meet the requirements of vehicle detection. These processes include grayscale conversion, histogram equalization, de-noising, binarization, morphological operation, and so on. Then the simulation results are obtained and compared, and the optimal processing method is selected. Then, the moving vehicle is detected, which is an important prerequisite for vehicle tracking and traffic information extraction. By comparing various methods and comparing the experimental results, the background difference method is selected, because the background differential method can obtain the complete vehicle. Moreover, this method can be used in the scene with high real-time requirement because of its small computational complexity. After that, the background modeling is needed. By improving the common background modeling methods, the stationary or slow moving vehicles will not be regarded as the background, and the background can be obtained quickly and accurately. In view of the existence of vehicle shadow, the common methods of vehicle shadow removal are improved. This method is more effective than the usual shadow removal method, and the shadow removal is complete and does not lose vehicle information. Secondly, this paper realizes the vehicle tracking, which plays a key role in the next vehicle information extraction. By comparing the tracking method, this paper selects the Camshift algorithm to track the vehicle, and introduces the Kalman filter to estimate the next motion of the vehicle, which reduces the range of the vehicle position search, and speeds up the calculation speed to a great extent. It makes vehicle tracking more efficient, and because of the characteristics of Camshift algorithm, the problem of vehicle occlusion can be solved to some extent. Finally, this paper extracts the traffic information, including the measurement of speed, the acquisition of traffic flow, the detection of whether the vehicle stops illegally and whether the vehicle is retrograde, and the traffic information to be obtained. According to the results, it can be concluded that the accuracy and speed of these methods can meet the requirements. In vehicle speed measurement, the commonly used vehicle speed measurement method is improved to make the speed measurement more accurate. In the detection of illegal parking, the commonly used detection method of illegal parking is improved. The detection speed of this method is fast, and it is suitable for use in the scene with high performance requirements in the surveillance video.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U495;TP391.41

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