【摘要】：為了提高分布場跟蹤算法的運算效率,增強其在復(fù)雜背景下的魯棒性,提出基于自適應(yīng)分層結(jié)構(gòu)的壓縮分布場跟蹤算法.該方法充分考慮目標(biāo)區(qū)域像素值分布情況,引入k-means算法對首幀標(biāo)記的目標(biāo)區(qū)域進行聚類分析,根據(jù)聚類結(jié)果自適應(yīng)的產(chǎn)生分布場結(jié)構(gòu).針對分布場模型維數(shù)較高的缺點,融合壓縮感知方法對分布場進行壓縮,降低模型維數(shù),提高算法效率.此外,改變原始分布場跟蹤算法采用的局部搜索跟蹤策略,利用隨機抽樣的方式來提高算法跟蹤精度.實驗結(jié)果表明,提出的算法與當(dāng)前流行的跟蹤算法相比,具有更好的表現(xiàn).
[Abstract]:In order to improve the computational efficiency of distributed field tracking algorithm and enhance its robustness in complex background, a compressed distributed field tracking algorithm based on adaptive hierarchical structure is proposed. In this method, the pixel value distribution in the target region is fully considered, and the k-means algorithm is introduced to cluster the target region marked by the first frame. According to the clustering results, the distribution field structure is generated adaptively. In view of the high dimension of distributed field model, the compression sensing method is used to compress the distributed field, reduce the dimension of the model and improve the efficiency of the algorithm. In addition, the local search and tracking strategy used in the original distributed field tracking algorithm is changed and random sampling is used to improve the tracking accuracy of the algorithm. Experimental results show that the proposed algorithm has better performance than the current popular tracking algorithm.
【作者單位】： 國家數(shù)字交換系統(tǒng)工程技術(shù)研究中心;
【基金】：國家自然科學(xué)基金(No.61379151,No.61521003) 國家科技支撐計劃(No.2014BAH30B01) 河南省杰出青年基金(No.144100510001)
【分類號】：TP391.41

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1 叱干鵬飛;基于分布場模型的目標(biāo)跟蹤方法研究[D];西北農(nóng)林科技大學(xué);2014年

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