title: Mean shift trackers with cross-bin metrics.
abstract: Cross-bin metrics have been shown to be more suitable than bin-by-bin metrics for measuring the distance between histograms in various applications. In particular, a visual tracker that minimizes the earth mover's distance (EMD) between the candidate and reference feature histograms has recently been proposed. This tracker was shown to be more robust than the Mean Shift tracker, which employs a bin-by-bin metric. In each frame, the former tracker iteratively shifts the candidate location by one pixel in the direction opposite to the EMD's gradient until no improvement is made. This optimization process involves the clustering of the candidate feature density in feature space, as well as the computation of the EMD between the candidate and reference feature histograms after each shift of the candidate location. In this paper, alternative trackers that employ cross-bin metrics as well, but that are based on Mean Shift (MS) iterations, are derived. The proposed trackers are simpler and faster due to 1) the use of MS-based optimization, which is not restricted to single pixel shifts, 2) abstention from any clustering of feature densities, and 3) abstention from EMD computations in multidimensional spaces.
authors.affiliation: MSR Advanced Technology Labs Israel, Microsoft Research, Microsoft Israel R&D Center, Building 23, Matam Park, Haifa 31905, Israel. idol@microsoft.com
authors.forename: Ido
authors.initials: I
authors.lastname: Leichter
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D014785:Vision, Ocular / Q000502:physiology*