基于光流法的車輛檢測與跟蹤
發(fā)布時間：2019-04-23 13:04
【摘要】：近年來,在智能交通領(lǐng)域中,基于視頻的車輛檢測跟蹤已成為一個重要的研究方向。光流法在視頻運動分析等領(lǐng)域具有極其重要的基礎(chǔ)地位,能得到目標詳細的二維運動信息,但光流算法計算的不適定及不能實時運算等問題限制了其廣泛應(yīng)用,因此研究出一種能實時執(zhí)行且精度較高的光流算法對推廣此算法在實時領(lǐng)域的應(yīng)用顯得尤為重要。若能將實時光流技術(shù)應(yīng)用于智能交通領(lǐng)域,對智能交通的發(fā)展也大有裨益。本文詳細闡述、分析了微分光流法的基本原理,并據(jù)此提出了多幀光流模型和其線性化求解方法,簡化了光流的求解過程并提高了其估算精度;選擇GPU平臺做算法移植,解決光流的實時計算問題;根據(jù)所得實時稠密光流給出了車輛檢測跟蹤方案。首先,時域相關(guān)性在視頻分析中具有重要作用,但在估算光流時這一特性卻很少被應(yīng)用。針對這一情況,提出在H-S光流模型基礎(chǔ)上引入前向幀并加入時域相關(guān)性約束,從而構(gòu)造出多幀光流模型。同時,針對能量泛函線性化求解過程異常復雜的情況,提出運用迭代重加權(quán)最小二乘法(IRLS)簡化這一求解過程。其次,根據(jù)光流算法在像素間耦合性低這一特點,選擇對其進行GPU平臺移植。在計算統(tǒng)一設(shè)備(CUDA)架構(gòu)下通過多線程并行執(zhí)行方式同時對多個像素的光流值進行估算。在估算結(jié)果精度相當?shù)那闆r下,在GPU上執(zhí)行時間遠小于CPU上執(zhí)行時間。對于分辨率為640*480的視頻圖像可以達到實時性運算,能滿足一般的應(yīng)用要求。最后,給出一種改進的車輛檢測跟蹤方案。一方面,方案使用GPU平臺計算所得實時稠密光流,相較于特征光流法和區(qū)域稠密光流法,可獲得更加準確的全局運動信息,相較于幀差法等運動檢測算法,也能獲得更好的運動目標提取效果;另一方面,方案根據(jù)光流場所得到的速度矢量對車輛幀間的位置進行預(yù)測和匹配,能夠?qū)囕v在跟蹤過程中常見的狀態(tài)變化進行判斷和處理,在實驗中取得了預(yù)期的效果。
[Abstract]:In recent years, video-based vehicle detection and tracking has become an important research direction in the field of intelligent transportation. Optical flow method plays an important role in the field of video motion analysis. It can obtain the detailed two-dimensional motion information of the target. However, the problems such as the ill-posed calculation of optical flow algorithm and the impossibility of real-time operation limit its wide application. Therefore, it is very important to develop a real-time and high-precision optical flow algorithm to extend the application of this algorithm in real-time domain. If the real-time optical flow technology can be applied to the field of intelligent transportation, it will also be of great benefit to the development of intelligent transportation. In this paper, the basic principle of the differential optical flow method is analyzed in detail. Based on this, a multi-frame optical flow model and its linearization method are proposed, which simplifies the process of solving the optical flow and improves its estimation accuracy. The algorithm is transplanted on GPU platform to solve the problem of real-time calculation of optical flow, and the vehicle detection and tracking scheme is given according to the dense real-time optical flow obtained. First, time-domain correlation plays an important role in video analysis, but it is rarely used to estimate optical flow. In order to solve this problem, a multi-frame optical flow model is constructed by introducing forward frame and time-domain correlation constraint on the basis of HES optical flow model. At the same time, the iterative reweighted least square method (IRLS) is proposed to simplify the solution process in view of the complexity of the linearization process of the energy functional. Secondly, according to the low coupling between pixels, the optical flow algorithm is transplanted on GPU platform. The optical flow values of multiple pixels are estimated in parallel multi-thread execution mode under the (CUDA) architecture of computing unified device at the same time. Under the same precision, the execution time on GPU is much shorter than that on CPU. The video image with a resolution of 640 脳 480 can achieve real-time operation and can meet the general application requirements. Finally, an improved vehicle detection and tracking scheme is presented. On the one hand, the scheme uses the GPU platform to calculate the real-time dense optical flow. Compared with the characteristic optical flow method and the region dense optical flow method, the scheme can obtain more accurate global motion information, compared with the frame difference method and other motion detection algorithms. It can also get better effect of moving object extraction. On the other hand, the scheme predicts and matches the vehicle frame position according to the velocity vector obtained from the optical flow field, which can judge and deal with the common state changes in the tracking process of the vehicle, and achieves the expected effect in the experiment.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495;TP391.41
本文編號：2463494
[Abstract]:In recent years, video-based vehicle detection and tracking has become an important research direction in the field of intelligent transportation. Optical flow method plays an important role in the field of video motion analysis. It can obtain the detailed two-dimensional motion information of the target. However, the problems such as the ill-posed calculation of optical flow algorithm and the impossibility of real-time operation limit its wide application. Therefore, it is very important to develop a real-time and high-precision optical flow algorithm to extend the application of this algorithm in real-time domain. If the real-time optical flow technology can be applied to the field of intelligent transportation, it will also be of great benefit to the development of intelligent transportation. In this paper, the basic principle of the differential optical flow method is analyzed in detail. Based on this, a multi-frame optical flow model and its linearization method are proposed, which simplifies the process of solving the optical flow and improves its estimation accuracy. The algorithm is transplanted on GPU platform to solve the problem of real-time calculation of optical flow, and the vehicle detection and tracking scheme is given according to the dense real-time optical flow obtained. First, time-domain correlation plays an important role in video analysis, but it is rarely used to estimate optical flow. In order to solve this problem, a multi-frame optical flow model is constructed by introducing forward frame and time-domain correlation constraint on the basis of HES optical flow model. At the same time, the iterative reweighted least square method (IRLS) is proposed to simplify the solution process in view of the complexity of the linearization process of the energy functional. Secondly, according to the low coupling between pixels, the optical flow algorithm is transplanted on GPU platform. The optical flow values of multiple pixels are estimated in parallel multi-thread execution mode under the (CUDA) architecture of computing unified device at the same time. Under the same precision, the execution time on GPU is much shorter than that on CPU. The video image with a resolution of 640 脳 480 can achieve real-time operation and can meet the general application requirements. Finally, an improved vehicle detection and tracking scheme is presented. On the one hand, the scheme uses the GPU platform to calculate the real-time dense optical flow. Compared with the characteristic optical flow method and the region dense optical flow method, the scheme can obtain more accurate global motion information, compared with the frame difference method and other motion detection algorithms. It can also get better effect of moving object extraction. On the other hand, the scheme predicts and matches the vehicle frame position according to the velocity vector obtained from the optical flow field, which can judge and deal with the common state changes in the tracking process of the vehicle, and achieves the expected effect in the experiment.
【學位授予單位】：江蘇科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495;TP391.41
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