【摘要】：視頻目標(biāo)跟蹤在計算機視覺領(lǐng)域中有著十分重要的地位,在智能交通、公共安全、人工智能等多方面擁有廣闊的應(yīng)用前景。但是傳統(tǒng)的目標(biāo)跟蹤算法存在許多問題,例如受環(huán)境的影響比較大,當(dāng)運動目標(biāo)出現(xiàn)遮擋時容易出現(xiàn)跟蹤丟失無法重新捕獲目標(biāo)。如何對運動目標(biāo)進行有效、準(zhǔn)確的跟蹤是計算機視覺領(lǐng)域中一直關(guān)注的問題。本文首先研究了基于Codebook算法的運動目標(biāo)檢測算法,給出了算法的基本原理和性能特點。針對原始Codebook算法在運動目標(biāo)檢測中計算速度比較慢的問題,本文提出了一種基于顏色空間改進和參數(shù)優(yōu)化的Codebook運動目標(biāo)檢測算法。將原Codebook算法從RGB空間轉(zhuǎn)換到Y(jié)UV空間,并且通過分析將3個顏色空間通道減少到1個,同時用亮度差值代替原始最大、最小亮度參數(shù),通過引入碼字權(quán)重系數(shù)來刪減和優(yōu)化其他參數(shù),從而使得優(yōu)化后的Codebook算法在運動目標(biāo)檢測中能夠在具有較高準(zhǔn)確性的情況下也較大程度提高算法的計算速度。針對原始TLD算法在跟蹤過程中容易出現(xiàn)跟蹤漂移的問題,本文提出了一種基于關(guān)鍵特征點的TLD算法簡稱為STLD,采用包含豐富信息量的特征點來代替原TLD算法中的Grid均勻采樣,提高了運動目標(biāo)的特征采樣點的跟蹤準(zhǔn)確度,抑制了原TLD算法跟蹤漂移,同時也減少了采樣點的跟蹤丟失率,因此具有更好的漂移抑制效果和更快的運算速度,提高了算法的魯棒性。針對原始TLD算法在運動目標(biāo)出現(xiàn)遮擋或者發(fā)生形變時會導(dǎo)致跟蹤失敗的問題,本文提出一種基于Kalman濾波的特征點TLD算法簡稱為KSTLD,在STLD算法檢測器前段引入預(yù)測器,通過Kalman預(yù)測器對目標(biāo)位置進行預(yù)測,加強視頻序列前后幀中運動目標(biāo)位置的相關(guān)性。從而得到當(dāng)前視頻圖像中運動目標(biāo)所在位置的大致區(qū)域,該預(yù)測器結(jié)果和STLD算法檢測器中三級分類器進行結(jié)合,改善了在遮擋環(huán)境下,對運動目標(biāo)的檢測效果,提高了 STLD算法檢測器的準(zhǔn)確性和運算速度。
[Abstract]:Video target tracking plays an important role in the field of computer vision, and has a broad application prospect in intelligent transportation, public safety, artificial intelligence and so on. However, there are many problems in the traditional target tracking algorithm, such as being influenced by the environment. When the moving target is blocked, the tracking loss is easy to occur and the target can not be captured again. How to track moving targets effectively and accurately is always concerned in the field of computer vision. In this paper, the moving target detection algorithm based on Codebook algorithm is studied, and the basic principle and performance characteristics of the algorithm are given. In order to solve the problem of slow computing speed of the original Codebook algorithm in moving target detection, this paper proposes a new Codebook moving target detection algorithm based on color space improvement and parameter optimization. The original Codebook algorithm is transformed from RGB space to YUV space, and three color space channels are reduced to one by analyzing, and the original maximum and minimum luminance parameters are replaced by luminance difference. By introducing the codeword weight coefficient to delete and optimize other parameters, the optimized Codebook algorithm can improve the computational speed of the algorithm in the case of high accuracy. In order to solve the problem that the original TLD algorithm is prone to trace drift in the tracking process, a TLD algorithm based on the key feature points is proposed in this paper. In short, STLD, uses feature points with abundant information to replace the Grid uniform sampling in the original TLD algorithm. The tracking accuracy of the feature sampling points of moving targets is improved, and the original TLD algorithm is restrained, and the tracking loss rate of the sample points is also reduced, so it has better drift suppression effect and faster operation speed. The robustness of the algorithm is improved. In order to solve the problem that the original TLD algorithm can cause tracking failure when the moving object is occluded or deformed, a feature point TLD algorithm based on Kalman filter is proposed in this paper, which is referred to as KSTLD, introducing predictor in front of STLD algorithm detector. The target position is predicted by Kalman predictor to enhance the correlation between the moving target position in the frame before and after the video sequence. The result of the predictor is combined with the three-level classifier in the STLD algorithm detector to improve the detection effect of moving target in the shaded environment. The accuracy and speed of STLD detector are improved.
【學(xué)位授予單位】：揚州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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