1 / 1

Spatiograms Versus Histograms for Region-Based Tracking STAN BIRCHFIELD AND SRIRAM RANGARAJAN

Spatiograms Versus Histograms for Region-Based Tracking STAN BIRCHFIELD AND SRIRAM RANGARAJAN CLEMSON UNIVERSITY. An illustrative insight. Abstract. Tracking results.

leslie-rogers
Download Presentation

Spatiograms Versus Histograms for Region-Based Tracking STAN BIRCHFIELD AND SRIRAM RANGARAJAN

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Spatiograms Versus Histograms for Region-Based Tracking STAN BIRCHFIELD AND SRIRAM RANGARAJAN CLEMSON UNIVERSITY An illustrative insight Abstract Tracking results • We introduce the concept of a spatiogram, which is a generalization of a histogram that includes potentially higher order moments. A histogram is a zeroth-order spatiogram, while second-order spatiograms contain spatial means and covariances for each histogram bin. This spatial information still allows quite general transformations, as in a histogram, but captures a richer description of the target to increase robustness in tracking. We show how to use spatiograms in kernel-based trackers, deriving a mean shift • procedure in which individual pixels vote not only for the amount of shift but also for its direction. Experiments show improved tracking results compared with histograms, using • both mean shift and exhaustive local search. To compare histograms and spatiograms, three experiments were conducted. Three poses of a head Image generated from histogram Histograms and spatiograms Experiment #1 Using mean shift. Spatiogram is slightly better, but both lose the target when the head jerks quickly. SPATIOGRAM HISTOGRAM A discrete function (an image): Image generated from spatiogram Binary 2D formulation: The i th moment: Tracking by mean shift Experiment #2 Using local exhaustive search (6 x 6 x 1 in x, y, and scale), with gradient dot product. Spatiogram is less distracted by the background, but both succeed in maintaining the target. Histogram (no spatial information) HISTOGRAMS SPATIOGRAMS Σ Likelihood function: Spatiogram (some spatial Information) number of bins µ Experiment #2 Using local exhaustive search (6 x 6 x 1 in x, y, and scale), with gradient dot product. Spatiogram succeeds, while histogram fails. • The spatial histogram, or spatiogram, captures some spatial information about the target: • m is the spatial mean of all the pixels that contribute to the bin • S is the spatial covariance matrix of all the pixels that contribute to the bin • Spatiograms are between histograms (which contain no spatial information) and specific geometric models like SSD-based translation or affine (which maintain precise spatial information) Conclusion target location model target • Introduction of a novel concept: a higher-order histogram that captures a limited amount of spatial information (spatiogram) • Derivation of a mean shift procedure for spatiograms • Demonstration of improved tracking results when compared to histograms Mean shift update:

More Related