Abstract
As one of the most traditional tracking methods, particle filter has been improved in many previous tracking methods due to its non-Gaussian and non-linear distribution. Meanwhile, pure tracking methods cannot achieve good performance in complex tracking scenarios where there enormous deformation and occlusion occur. We present a combination of patch-based tracking and detecting methodology for visual tracking. In our tracking stage, a hierarchical patch-based histogram is used to describe the observation model, computed by an improved L 1 bin-ratio dissimilarity( L 1 -BRD) distance. While in the detecting stage, a patch-based binary feature is obtained through centersymmetric local binary pattern (CS-LBP) and then used to train a randomize fern forest. We combine the two parts collaboratively and experiments demonstrate that the proposed tracking framework outperforms the state-of-theart methods in challenging scenarios.
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Li, Q., Zhou, Y. (2013). Patch-Based Tracking and Detecting for Visual Tracking. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_98
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DOI: https://doi.org/10.1007/978-3-642-42057-3_98
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