Person tracking by integrating optical flow and uniform brightness regions

Abstract

Introducdion of them are based on subtraction his method cannot be applied to camera moves because the backion based method[2] works s, but object tracking is diffithe object changes. An optican be applied to such a case.

Tracking individuals in surveillance video of a high-density crowd

“…Initial approaches assume that optical flow on the target is uniform, and the target is tracked by computing the mean flow around the target location [27] [30]. Denman et al [8] extended this approach by using foreground-background segmentation to get a precise target region.…”

A Multi-Class Tracker Using a Scalable Condensation Filter

“…Single class person detectors are far more common and typically use some form of background segmentation [13,5,3], or optical flow [11,8] as a basis for tracking; and use Kalman filters, or motion models (first or second order) to track and predict object positions. Haritaoglu et al…”

“…Rather than tracking blobs Zhao et al [13] proposed a system that used an ellipsoid shape model to locate and segment people from the motion image. Yamane et al [11] proposed a method using optical flow and uniform brightness regions (a section where the optical flow cannot be detected) to track people, while Okada et al [8] combined optical flow and depth information to track.…”

Robust tracking of persons in real-world scenarios using a statistical computer vision approach

“…There are several examples for the successful use of this approach (see, e.g. [5,11,13,24]). For instance, the system presented in Ref.…”