基于增強(qiáng)群跟蹤器和深度學(xué)習(xí)的目標(biāo)跟蹤
doi: 10.11999/JEIT141362
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1.
(長(zhǎng)春理工大學(xué)電子信息工程學(xué)院 長(zhǎng)春 130022) ②(中國科學(xué)院長(zhǎng)春光學(xué)精密機(jī)械與物理研究所 長(zhǎng)春 130000) ③(東北師范大學(xué)計(jì)算機(jī)科學(xué)與信息技術(shù)學(xué)院 長(zhǎng)春 130117)
國家自然科學(xué)基金(61172111)和吉林省科技廳項(xiàng)目(20090512, 20100312)資助課題
Target Tracking Based on Enhanced Flock of Tracker and Deep Learning
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(School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China)
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摘要: 為解決基于外觀模型和傳統(tǒng)機(jī)器學(xué)習(xí)目標(biāo)跟蹤易出現(xiàn)目標(biāo)漂移甚至跟蹤失敗的問題,該文提出以跟蹤-學(xué)習(xí)-檢測(cè)(TLD)算法為框架,基于增強(qiáng)群跟蹤器(FoT)和深度學(xué)習(xí)的目標(biāo)跟蹤算法。FoT實(shí)現(xiàn)目標(biāo)的預(yù)測(cè)與跟蹤,增添基于時(shí)空上下文級(jí)聯(lián)預(yù)測(cè)器提高預(yù)測(cè)局部跟蹤器的成功率,快速隨機(jī)采樣一致性算法評(píng)估全局運(yùn)動(dòng)模型,提高目標(biāo)跟蹤的精確度。深度去噪自編碼器和支持向量機(jī)分類器構(gòu)建深度檢測(cè)器,結(jié)合全局多尺度掃描窗口搜索策略檢測(cè)可能的目標(biāo)。加權(quán)P-N學(xué)習(xí)對(duì)樣本加權(quán)處理,提高分類器的分類精確度。與其它跟蹤算法相比較,在復(fù)雜環(huán)境下,不同圖片序列實(shí)驗(yàn)結(jié)果表明,該算法在遮擋、相似背景等條件下具有更高的準(zhǔn)確度和魯棒性。
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關(guān)鍵詞:
- 計(jì)算機(jī)視覺 /
- 群跟蹤器 /
- 跟蹤-學(xué)習(xí)-檢測(cè) /
- 深度學(xué)習(xí) /
- 支持向量機(jī) /
- 深度檢測(cè)器
Abstract: To solve the problem that the tracking algorithm often leads to drift and failure based on the appearance model and traditional machine learning, a tracking algorithm is proposed based on the enhanced Flock of Tracker (FoT) and deep learning under the Tracking-Learning-Detection (TLD) framework. The target is predicted and tracked by the FoT, the cascaded predictor is added to improve the precision of the local tracker based on the spatio-temporal context, and the global motion model is evaluated by the speed-up random sample consensus algorithm to improve the accuracy. A deep detector is composed of the stacked denoising autoencoder and Support Vector Machine (SVM), combines with a multi-scale scanning window with global search strategy to detect the possible targets. Each sample is weighted by the weighted P-N learning to improve the precision of the deep detector. Compared with the state-of-the-art trackers, according to the results of experiments on variant challenging image sequences in the complex environment, the proposed algorithm has more accuracy and better robust, especially for the occlusions, the background clutter and so on. -
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