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This paper presents a modified Kanade-Lucas-Tomasi (KLT) tracking framework for multiple objects tracking applications. First, the framework includes a global pixel-level probabilistic model and an adaptive RGB template model to modify traditional KLT tracker more robust to track multiple objects and partial occlusions. Meanwhile, a Merge and Split algorithm is introduced in the proposed framework...
In this paper we present an effective and fast tracking algorithm, in which object tracking is achieved by solving L2-regularized least square (L2-RLS) problem-s within a Bayesian inference framework. Firstly, we model the appearance of the tracked target with P-CA basis vectors and square templates which make the tracker not only exploit the strength of sub space repre-senation but also explicitly...
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