Dynamic Scenario Segmentation based on Target Assumption Sort

This paper enables robust foreground segmentation by sorting target proposals over a assumption space to achieve consistent target candidates and binary segmentation of a video sequence. This is followed by sorting the target candidates over a specific hypothesis space to yield consistent and dense object proposals. An efficient higher-order graph-cut method is adopted to optimize a Markov Random Field (MRF) model, which is instantiated by the estimated foreground hypothesis with the highest score. Compared with a state-of-the-art algorithm, our method results in better and robust segmentation performance when dealing with highly dynamic image sequences. Introduction Imagine a scenario where an autonomous robot is wondering in an unknown environment, the location information of foreground targets which can be inferred with an unsupervised method will certainly facilitate, to a large extent, subsequent recognition, tracking as well as visual servo manipulation of the targets. Despite the research efforts in dynamic scene segmentation, designing an algorithm that is robust for a wide variety of dynamic scenes that involve complex natural environments is still an open problem. The bottle neck in dynamic scene segmentation is the foreground object hypothesis stage. Currently, there are mainly two categories of methods for foreground hypothesis[1]. One approach takes advantage of such object appearance cues as color and texture. Typically, this approach consists of bottom-up and top-down segmentation paradigms. The bottom-up [2] paradigm exploits the appearance similarity of neighboring pixels, which are then merged into hyper pixels or image patches. Actually, bottom-up segmentation based on appearance cues always leads to over segmentation of dynamic scenes and cannot segment foreground objects from background scenes. Target candidates generation Due to the highly dynamic and complex nature of natural scenes, a set of target candidates are initially generated in order to form a pool of assumption. Instead of using deformable part model based object detection approaches, which produce object candidates that are represented in terms of image windows, we perform segmentation based object hypothesis by using low-level appearance features computed on super-pixels. We first compute super-pixels of the original images using SLIC algorithm[3]. Moreover, a specific energy function is formulated instead a set of parametric functions. In precise, we compute a binary segmentation by minimizing the energy: = + ∑ ∑ ( , ) ( ) ( ) ( , ) u u v u u v E X U x V x x (1) where u and v are indicx of super-pixels. U and V represent unary and pairwise potentials of the energy function E. For each super-pixel u. Pairwise potentials are used to encode smoothness between super-pixels. Since the global probability boundary (gPb) algorithm [4] has relatively good performance in detecting salient contours in whole images, thus the standard CPMC algorithm employs gPb features to capture pairwise potentials. International Conference on Intelligent Systems Research and Mechatronics Engineering (ISRME 2015) © 2015. The authors Published by Atlantis Press 1415 Target assumption sorting The target of sorting is to assign assumption that exhibit target-like regularities with higher sorting score. The feature consists of graph partition properties, region properties such as area, perimeter, bounding box location, major and minor axis lengths of the ellipse as well as Gestalt properties such as curvilinear continuity and convexity. Gestalt properties are computed in terms of 2 χ distance, which is based on color histogram measures between two half discs of a circular image patch. 2 2 ( ) ( , ) χ − = + ∑ i i i i i h g h g h g (2) where h and g represent color histograms of two half discs. The radius of each disc is fixed at 6 pixels, a value that was empirically seen to yield good results throughout the paper. Spatio-temporal MRF segmentation The popular spatio-temporal Markov random field (MRF) is used to model the correlation in spatial domain an along temporal axis. In particular, the MRF model consists of two unary terms and two pairwise terms. The unary terms exert appearance and position constraints on foreground object hypotheses. Following the method in [1], we formulate MRF model as