无感觉 发表于 2025-3-21 16:53:05
书目名称Computer Vision -- ACCV 2012影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0234110<br><br> <br><br>书目名称Computer Vision -- ACCV 2012读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0234110<br><br> <br><br>禁止,切断 发表于 2025-3-21 20:55:48
Visual Tracking in Continuous Appearance Space via Sparse Coding dynamic change of object appearance by adaptively updating the object template model using the learned dictionary, and at the same time can avoid drifting by using representation error for supervision. Our method thus can perform more robust than previous methods in dynamic scenes of gradual change表示向下 发表于 2025-3-22 02:40:34
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Robust Registration-Based Tracking by Sparse Representation with Model Update algorithm which iteratively solves the LASSO and classical Lucas-Kanade by optimizing one while keeping another fixed. Unlike existing sparsity-based work that uses exemplar templates as the object model, we explore the low-dimensional linear subspace of the object appearances for object representaALIBI 发表于 2025-3-22 14:37:03
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Royal Navy Metric Warning Radar, 1935–45tracklet belonging to the same object. Furthermore, we give a near-optimal algorithm based on globally greedy strategy to deal with spatio-temporal clustering, which runs linearly with the number of tracklets. We quantitatively evaluate the performance of our method on three challenging data sets an