【摘要】：基于計(jì)算機(jī)視覺的車輛跟蹤多年來一直是熱門的研究課題之一，它是智能交通的基礎(chǔ)。視覺車輛跟蹤技術(shù)涉及到諸多學(xué)科，它不僅與圖像處理技術(shù)、計(jì)算機(jī)視覺息息相關(guān)，還與人工智能和模式識(shí)別等緊密聯(lián)系。雖然目前有目標(biāo)跟蹤算法在車輛跟蹤上的應(yīng)用，但是目標(biāo)車輛本身特征的可變性、背景的干擾、跟蹤過程中目標(biāo)的遮擋問題以及車輛的快速運(yùn)動(dòng)都成為了影響跟蹤準(zhǔn)確性的因素。因此，研究一種準(zhǔn)確度高、魯棒性強(qiáng)的車輛跟蹤算法依然是目前迫切需要解決的問題。 本文首先介紹了車輛跟蹤在國內(nèi)外的研究現(xiàn)狀，接著研究了傳統(tǒng)Mean-Shift算法在車輛跟蹤中的應(yīng)用。接著本文針對(duì)車輛跟蹤中目標(biāo)尺度變化、背景干擾、遮擋及目標(biāo)快速運(yùn)動(dòng)等問題，在基于顏色特征的Mean-Shift算法基礎(chǔ)上，結(jié)合目標(biāo)信息和運(yùn)動(dòng)估計(jì)成功實(shí)現(xiàn)了車輛跟蹤。由于目標(biāo)車輛在移動(dòng)的過程中尺度可能發(fā)生變化，或者被其它干擾物遮擋，這就使目標(biāo)模型與候選模型之間的相似性系數(shù)降低，導(dǎo)致Mean-Shift算法陷入局部最優(yōu)，從而造成定位失敗。在本文中，在Mean-Shift算法基礎(chǔ)上，結(jié)合了目標(biāo)的信息，提高了Mean-Shift算法對(duì)目標(biāo)尺度變化的適應(yīng)性并優(yōu)化了模型；當(dāng)目標(biāo)被嚴(yán)重遮擋時(shí)，結(jié)合運(yùn)動(dòng)估計(jì)，利用卡爾曼濾波預(yù)測目標(biāo)的位置，從而彌補(bǔ)了Mean-Shift算法在處理遮擋問題時(shí)的不足。此外，，本文還針對(duì)Mean-Shift在跟蹤快速移動(dòng)的目標(biāo)車輛容易陷入局部最優(yōu)的問題，利用卡爾曼濾波器優(yōu)化后的初始中心克服了基本Mean-Shift算法用泰勒級(jí)數(shù)估計(jì)當(dāng)前幀初始窗口精度不高的缺陷。最后實(shí)驗(yàn)結(jié)果表明，改進(jìn)的Mean-Shift算法能準(zhǔn)確的對(duì)目標(biāo)進(jìn)行跟蹤。
[Abstract]:Vehicle tracking based on computer vision has been one of the hot research topics for many years. It is the basis of intelligent transportation. Visual vehicle tracking involves many disciplines. It is not only closely related to image processing and computer vision, but also closely related to artificial intelligence and pattern recognition. Although there are some applications of the target tracking algorithm in vehicle tracking, the variability of the target vehicle's own characteristics and the interference of the background, The occlusion of the target and the rapid movement of the vehicle are the factors that affect the tracking accuracy. Therefore, it is still an urgent problem to study a vehicle tracking algorithm with high accuracy and robustness. This paper first introduces the research status of vehicle tracking at home and abroad, and then studies the application of traditional Mean-Shift algorithm in vehicle tracking. Then, aiming at the problems of target scale change, background interference, occlusion and fast moving of target in vehicle tracking, this paper successfully realizes vehicle tracking based on color feature based Mean-Shift algorithm, combined with target information and motion estimation. Because the scale of the target vehicle may change in the course of moving, or be blocked by other interference, the similarity coefficient between the target model and the candidate model will be reduced, and the Mean-Shift algorithm will fall into the local optimum. As a result, the location fails. In this paper, based on the Mean-Shift algorithm, combining the information of the target, the adaptability of the Mean-Shift algorithm to the change of the target scale is improved and the model is optimized. When the target is heavily occluded, the Kalman filter is used to predict the location of the target in combination with motion estimation, which makes up for the deficiency of the Mean-Shift algorithm in dealing with the occlusion problem. In addition, this paper also aims at the problem that Mean-Shift is prone to fall into local optimum in tracking fast moving target vehicles. Using the Kalman filter to optimize the initial center overcomes the defects of the basic Mean-Shift algorithm which uses Taylor series to estimate the current frame initial window with low accuracy. Finally, the experimental results show that the improved Mean-Shift algorithm can track the target accurately.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U495;TP391.41

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