本文選題：視頻目標(biāo)跟蹤 + Mean��； 參考：《太原科技大學(xué)》2017年碩士論文

【摘要】：視頻目標(biāo)跟蹤是機器視覺領(lǐng)域中一個十分重要且極具挑戰(zhàn)的研究方向,在智能視頻監(jiān)控、智能交通、氣象分析、遠(yuǎn)距離測量以及醫(yī)學(xué)圖像分析等方面有著廣泛的應(yīng)用。然而在實際應(yīng)用中,由于目標(biāo)外觀和周圍環(huán)境不斷地變化,令大多數(shù)現(xiàn)有跟蹤算法在復(fù)雜環(huán)境下無法實現(xiàn)高性能跟蹤。因此,不斷提高目標(biāo)跟蹤算法在跟蹤過程中的穩(wěn)定性和準(zhǔn)確性是現(xiàn)階段這一領(lǐng)域的研究熱點和重點改進方向。本文主要對動態(tài)場景下目標(biāo)的跟蹤問題進行了研究。該問題的關(guān)鍵在于檢測和跟蹤剛性物體以及非剛性物體的變化,并對目標(biāo)進行時實跟蹤。由于在實際的復(fù)雜環(huán)境中,目標(biāo)存在不同尺度的變化,以及相似目標(biāo)會出現(xiàn)跟蹤錯誤等情況,而基于Mean Shift核函數(shù)的方法能夠?qū)Φ途S度數(shù)據(jù)在高維度進行有效區(qū)分。因而本文主要闡述Mean Shift算法在目標(biāo)跟蹤中存在的問題。研究了相似目標(biāo)在復(fù)雜環(huán)境中出現(xiàn)漂移的問題,提出在核函數(shù)的基礎(chǔ)上加入了色彩空間距離的方法,實現(xiàn)了色彩距離在高維空間的有效區(qū)分,用以解決背景干擾引起的跟蹤不準(zhǔn)確以及實時性問題,與傳統(tǒng)方法相比,能夠更精確的進行視頻跟蹤。針對實際場景中機動目標(biāo)存在的尺度變化問題,在核函數(shù)的基礎(chǔ)上融入色彩空間距離,利用巴氏距離來檢測目標(biāo)大小的變化,實現(xiàn)了核函數(shù)窗口的自適應(yīng)性,從而解決了由于跟蹤目標(biāo)大小變化引起的跟蹤不精確和不穩(wěn)定問題。針對視頻跟蹤算法是否需要時實處理,是否具有可移植性問題,在VS2013下對OpenCV2.4.9進行配置,并完成了系統(tǒng)軟件平臺的搭建,利用這一平臺對本文提出的跟蹤算法進行實現(xiàn)及結(jié)果分析,通過與傳統(tǒng)的Mean Shift算法進行比較,可以分析得出,將色彩空間距離和巴氏距離的優(yōu)勢相結(jié)合應(yīng)用到視頻跟蹤后,可有效提升在復(fù)雜場景下目標(biāo)尺度變化引起的不準(zhǔn)確性。最終本文實現(xiàn)了在復(fù)雜背景下高準(zhǔn)確度的跟蹤,同時做到了核函數(shù)窗口大小的自適應(yīng)調(diào)整,通過實驗的對比,說明跟蹤過程的實時性和準(zhǔn)確性得到進一步提升,并在OpenCV平臺中穩(wěn)定運行,為其在實際應(yīng)用打下了堅實的基礎(chǔ)。
[Abstract]:Video target tracking is a very important and challenging research field in the field of machine vision. It is widely used in intelligent video surveillance, intelligent transportation, meteorological analysis, remote measurement and medical image analysis. However, in practical applications, most of the existing tracking algorithms are unable to achieve high performance tracking in complex environments due to the continuous changes in the appearance of the target and the surrounding environment. Therefore, improving the stability and accuracy of target tracking algorithm in the process of tracking is a hot research topic and an important improvement direction in this field. In this paper, the problem of target tracking in dynamic scene is studied. The key to this problem is to detect and track the changes of rigid and non-rigid objects and to track the target in real time. Due to the fact that the target has different scales in the complex environment, and the similar target will have tracking errors, the method based on Mean Shift kernel function can effectively distinguish the low-dimensional data from the high-dimensional data. Therefore, this paper mainly describes the problem of Mean Shift algorithm in target tracking. In this paper, the problem of the drift of similar targets in complex environment is studied, and the method of adding color space distance based on kernel function is put forward to realize the effective distinction of color distance in high dimensional space. In order to solve the problem of inaccuracy and real-time caused by background interference, video tracking is more accurate than traditional methods. Aiming at the problem of scale change of maneuvering target in actual scene, the color space distance is incorporated into the kernel function, and the change of target size is detected by using the pasteurian distance, and the self-adaptability of kernel function window is realized. Thus, the tracking inaccuracy and instability caused by the change of target size are solved. Aiming at whether the video tracking algorithm needs real processing and portability, the OpenCV2.4.9 is configured under VS2013, and the system software platform is built. By using this platform, the tracking algorithm proposed in this paper is implemented and the results are analyzed. By comparing with the traditional Mean Shift algorithm, it can be concluded that the advantages of color space distance and pasteurian distance are applied to video tracking. It can effectively improve the inaccuracy caused by the change of target scale in complex scene. Finally, this paper realizes the tracking with high accuracy in complex background and adaptively adjusts the window size of kernel function. Through the comparison of experiments, it shows that the real-time and accuracy of the tracking process are further improved. And run stably in OpenCV platform, lay a solid foundation for its practical application.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前9條

1 毛曉波;郝向東;梁靜;;基于ELM與Mean Shift的抗遮擋目標(biāo)跟蹤算法[J];鄭州大學(xué)學(xué)報(工學(xué)版);2016年01期

2 徐火希;;基于改進Mean Shift的運動目標(biāo)跟蹤算法[J];兵器裝備工程學(xué)報;2016年02期

3 聶義;陽波;廖武;;一種改進的meanshift目標(biāo)跟蹤算法[J];電腦與信息技術(shù);2015年05期

4 陳定坤;楊艷;;一種改進的Mean-shift運動目標(biāo)跟蹤算法[J];半導(dǎo)體光電;2015年01期

5 Myoung-beom CHUNG;Il-ju KO;;Identical-video retrieval using the low-peak feature of a video's audio information[J];Journal of Zhejiang University-Science C(Computer & Electronics);2010年03期

6 李鵬飛;陳朝武;李曉峰;;智能視頻算法評估綜述[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2010年02期

7 李立仁;李少軍;劉忠領(lǐng);;智能視頻監(jiān)控技術(shù)綜述[J];中國安防;2009年10期

8 吳成東;劉o，

本文編號：1902384