本文選題：運動目標(biāo)識別 + 光流法��； 參考：《中國海洋大學(xué)》2014年碩士論文

【摘要】：近年來，隨著與汽車、交通等相關(guān)技術(shù)的不斷成熟和人民生活質(zhì)量的大幅提高，城市化進(jìn)程加快，我國家庭對汽車的擁有量急速增長，城市道路交通越來越擁堵，行車速度也越來越快，交通矛盾日益突出，交通事故頻發(fā)，給人們的生命財產(chǎn)和國民經(jīng)濟(jì)造成了巨大損失。因此自動駕駛和安全駕駛越來越成為研究的熱點，智能交通系統(tǒng)中車輛檢測與跟蹤技術(shù)的研究也越來越受到國內(nèi)外學(xué)者們的高度關(guān)注。 基于視頻的運動物體識別算法一直是計算機視覺研究的熱點。各國的研究人員對這個問題從不同角度給出了各異的解決方法，然而由于基于視頻的運動目標(biāo)識別的復(fù)雜性，在識別的實時性及識別精度上還有很多難點，還沒有找到一種特定的方法適合所有的場景，特別是對于雨、雪、霧等非常規(guī)天氣，對運動目標(biāo)的識別仍然存在困難。 本文首先分析了常用的基于視頻的主要運動目標(biāo)識別的研究意義與研究現(xiàn)狀，并對常用檢測算法進(jìn)行了分析和比較，對特征提取和基于時域的跟蹤方法進(jìn)行了詳細(xì)的介紹，對傳統(tǒng)的檢測跟蹤算法同基于特征的光流法的優(yōu)劣進(jìn)行比較。接著對基于高斯分布的背景建模做了介紹，后面利用隱馬爾可夫模型完成軌跡跟蹤。 最后，在結(jié)合國內(nèi)外研究成果的基礎(chǔ)上，，本文提出了一種適合于霧霾天的運動目標(biāo)識別方法，該方法能在光線不好，遮擋嚴(yán)重的霧霾天較好的完成運動目標(biāo)的識別。同時進(jìn)行實驗驗證，并對實驗結(jié)果進(jìn)行分析。通過實驗，可以驗證基于特征的光流法及對運動目標(biāo)進(jìn)行低維特征提取的識別跟蹤算法在實時性和準(zhǔn)確性上有較好的表現(xiàn)。同時，也對目前仍存在的問題進(jìn)行了分析，同時對下一步的研究方向提出展望。 綜上，本文提出的基于視頻的運動目標(biāo)識別方法，在確保識別精度的前提下提出了在惡劣的非常規(guī)天氣下運動目標(biāo)的識別方法，并驗證了該方法的實用性。
[Abstract]:In recent years, with the continuous maturation of related technologies such as automobile, traffic and other related technologies and the substantial improvement of people's quality of life, the process of urbanization has been accelerated, the number of Chinese families owning cars has increased rapidly, and the urban road traffic has become more and more congested. The speed of driving is also getting faster and faster, the traffic contradiction is more and more prominent, the traffic accident frequently occurs, has caused the huge loss to the people's life and property and the national economy. Therefore, automatic driving and safe driving are becoming more and more research hotspot, and the research of vehicle detection and tracking technology in intelligent transportation system has been paid more and more attention by scholars at home and abroad. Video-based moving object recognition algorithm has been the focus of computer vision research. Researchers from different countries have given different solutions to this problem from different angles. However, because of the complexity of moving target recognition based on video, there are still many difficulties in real-time recognition and recognition accuracy. A specific method has not been found for all scenarios, especially for rain, snow, fog and other unconventional weather, so it is still difficult to identify moving targets. This paper first analyzes the research significance and research status of the main moving target recognition based on video, and then analyzes and compares the common detection algorithms, and introduces the feature extraction and time-domain tracking methods in detail. The advantages and disadvantages of the traditional detection and tracking algorithm are compared with the feature based optical flow method. Then the background modeling based on Gao Si distribution is introduced, and then the hidden Markov model is used to track the trajectory. Finally, based on the research results at home and abroad, this paper proposes a moving target recognition method suitable for smog days. This method can be used to recognize moving targets in poor light and severe smog days. At the same time, the experimental verification and analysis of the experimental results are carried out. The experiments show that the feature based optical flow method and the low dimensional feature extraction algorithm for moving targets have good performance in real time and accuracy. At the same time, the existing problems are analyzed, and the future research direction is prospected. In summary, the method of moving target recognition based on video is proposed in this paper, and the method of moving target recognition in bad unconventional weather is put forward, and the practicability of this method is verified.
【學(xué)位授予單位】：中國海洋大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 李喜來;李艾華;白向峰;;智能交通系統(tǒng)運動車輛的光流法檢測[J];光電技術(shù)應(yīng)用;2010年02期

2 王萬國;王濱海;王振利;李麗;;Mean Shift跟蹤算法中核函數(shù)參數(shù)的評估與分析[J];光學(xué)與光電技術(shù);2012年02期

相關(guān)博士學(xué)位論文 前1條

1 徐旭;復(fù)雜環(huán)境下的車輛目標(biāo)跟蹤技術(shù)研究[D];吉林大學(xué);2013年

本文編號：2063327