Abstract
Improved Mean Shift Target Localization using True Background Weighted Histogram and Geometric Centroid Adjustment
Highlights
Object tracking is an important and challenging aspect in computer vision applications
The existing Mean shift (MS) [4], Corrected background weighted histogram (CBWH) [7] and proposed tracking algorithms are evaluated on different challenging video sequences including videos from Bonn Benchmark on tracking (BoBoT) and vivid tracking dataset
The background weighted histogram transformation applied by the CBWH and true background weighted histogram (TBWH) scheme is shown in Fig. 5(c) and Fig. 5(d) respectively
Summary
Object tracking is an important and challenging aspect in computer vision applications. MS is a deterministic iterative procedure for locating the maxima of a density function given discrete data samples [8]. In tracking applications it compares a target model density function with current frame to find out the most promising converging region [4]. In complex scenarios (i.e. same background and target features) it fails to represent the best nonparametric density estimate [3], [4], [6] causes error in target localization. The MS algorithm sometimes fail to properly estimate target center (due to presence of local maxima), resulting in incorrect initialization for the frame this insufficiency would often cause false convergence
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