Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters
Conventional tracking-by-detection approaches for visual object tracking often assume that the task at hand is a binary foreground-versus-background classification problem where the background is a single, generic, and all-inclusive class. In contrast, here we argue that the background appearance for the most part possesses a more complicated structure that will benefit from further partitioning into multiple contextual clusters. Our observation is that, although the background class is...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Circuits and Systems for Video Technology|
|Access Rights:||Open Access|
|01_Zhu_Not_All_Negatives_Are_Equal%3A_2018.pdf||6.12 MB||Adobe PDF|
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