【摘要】：視覺跟蹤中,高效魯棒的特征表達是解決復雜環(huán)境下跟蹤漂移問題的關鍵。該文針對深層網絡預訓練復雜費時及單網絡跟蹤易漂移的問題,在粒子濾波框架下,提出一種基于自適應深度稀疏網絡的在線跟蹤算法。該算法利用Re LU激活函數(shù),針對不同類型目標構建了一種具有自適應選擇性的深度稀疏網絡結構,僅通過有限標簽樣本的在線訓練,就可得到魯棒的跟蹤網絡。實驗數(shù)據表明:與當前主流的跟蹤算法相比,該算法的平均跟蹤成功率和精度均為最好,且與同樣基于深度學習的DLT算法相比分別提高了20.64%和17.72%。在光照變化、相似背景等復雜環(huán)境下,該算法表現(xiàn)出了良好的魯棒性,能夠有效地解決跟蹤漂移問題。
[Abstract]:In visual tracking, efficient and robust feature representation is the key to solve the problem of tracking drift in complex environment. Aiming at the complex and time-consuming pre-training of deep network and the easy drift of single network tracking, this paper proposes an online tracking algorithm based on adaptive deep sparse network under the framework of particle filter. Using the Re LU activation function, the algorithm constructs a kind of self-adaptive and selective deep sparse network structure for different types of targets. The robust tracking network can be obtained only by the online training of finite tag samples. The experimental data show that the average tracking success rate and accuracy of the algorithm are the best compared with the current mainstream tracking algorithms, and the DLT algorithm based on the same depth learning is increased by 20.64% and 17.72% respectively. In complex environments such as illumination variation and similar background, the proposed algorithm is robust and can effectively solve the drift tracking problem.
【作者單位】： 空軍工程大學信息與導航學院;
【基金】：國家自然科學基金(61473309) 陜西省自然科學基礎研究計劃項目(2015JM6269,2016JM6050)~~
【分類號】：TP391.41

【相似文獻】

相關期刊論文 前1條

1 程健,曾以亮;IPv6在產品特性在線跟蹤中的應用[J];機械與電子;2004年07期

，

本文編號：2263851