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Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning
2020
Mathematical Problems in Engineering
Object tracking is a critical research in computer vision and has attracted significant attention over the past few years. However, the traditional object tracking algorithms often suffer from the object drifting problem due to various challenging factors in complex environments such as object occlusion and background clutter. This paper proposes a robust and effective object tracking algorithm, called MCM, which combines compressive sensing and online multiple instance learning in a
doi:10.1155/2020/1574054
fatcat:qxwp7n4wdfbbrbipmux2t7msf4