Multiple cues-based active contours for target contour tracking under sophisticated background

Abstract

In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a twolayer-based target appearance model that combines both discriminative pre-learned-based global layer and votingbased local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment. Electronic supplementary materialThe online version of this article (

Visual contour tracking based on inner-contour model particle filter under complex background

“…Most contour tracking methods can be grouped into two categories, parametric active contour [6][7][8] and geometric active contour [5,9], with different representations of contour curves. In the former, the contour was approximated by an explicit parametric model, typically using a set of control points and B-splines.…”

“…Furthermore, rectangle or other rigid shapes contain some background pixels outside the real target region, which will reduce the robustness of tracking. To overcome this problem, some researchers use contours to represent deformable targets [2][3][4][5].…”