本文選題：加速魯棒特征 + 多示例學(xué)習(xí)　； 參考：《計算機應(yīng)用》2016年11期

【摘要】：針對照明變化、形狀變化、外觀變化和遮擋對目標(biāo)跟蹤的影響,提出一種基于加速魯棒特征(SURF)和多示例學(xué)習(xí)(MIL)的目標(biāo)跟蹤算法。首先,提取目標(biāo)及其周圍圖像的SURF特征;然后,將SURF描述子引入到MIL中作為正負(fù)包中的示例;其次,將提取到的所有SURF特征采用聚類算法實現(xiàn)聚類,建立視覺詞匯表;再次,通過計算視覺字在多示例包的重要程度,建立"詞-文檔"矩陣,并且求出包的潛在語義特征通過潛在語義分析(LSA);最后,通過包的潛在語義特征訓(xùn)練支持向量機(SVM),使得MIL問題可以依照有監(jiān)督學(xué)習(xí)問題進(jìn)行解決,進(jìn)而判斷是否為感興趣目標(biāo),最終實現(xiàn)視覺跟蹤的目的。通過實驗,明確了所提算法對于目標(biāo)的尺度縮放以及短時局部遮擋的情況都有一定的魯棒性。
[Abstract]:Aiming at the influence of illumination change, shape change, appearance change and occlusion on target tracking, a target tracking algorithm based on accelerated robust feature tracking (surf) and multi-example learning algorithm (MIL) is proposed.Firstly, the SURF features of the target and its surrounding images are extracted; then, the SURF descriptor is introduced into the MIL as an example of positive and negative packets. Secondly, all the extracted SURF features are clustered by clustering algorithm to establish the visual vocabulary.By calculating the importance of visual words in multi-sample packets, the "word-document" matrix is established, and the potential semantic features of the packets are obtained through potential semantic analysis.By training support vector machines with the potential semantic features of packets, the MIL problem can be solved according to supervised learning problems, and then determine whether it is the object of interest, and finally achieve the purpose of visual tracking.Through experiments, it is clear that the proposed algorithm is robust to the scale scaling of the target and the local occlusion in a short time.
【作者單位】： 山西大學(xué)計算機與信息技術(shù)學(xué)院;西安工程大學(xué)計算機科學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61201453,61201118) 山西省基礎(chǔ)研究計劃項目(2014021022-2) 山西省高等學(xué)校科技創(chuàng)新項目(2015108)~~
【分類號】：TP391.41
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本文編號：1731053