Region-Level Motion-Based Background Modeling and Subtraction Using MRFs

Abstract

This paper presents a new approach to automatic segmentation of foreground objects from an image sequence by integrating techniques of background subtraction and motion-based foreground segmentation. First, a region-based motion segmentation algorithm is proposed to obtain a set of motion-coherence regions and the correspondence among regions at different time instants. Next, we formulate the classification problem as a graph labeling over a region adjacency graph based on Markov random fields (MRFs) statistical framework. A background model representing the background scene is built and then is used to model a likelihood energy. Besides the background model, a temporal coherence is also maintained by modeling it as the prior energy. On the other hand, color distributions of two neighboring regions are taken into consideration to impose spatial coherence. Then, the a priori energy of MRFs takes both spatial and temporal coherence into account to maintain the continuity of our segmentation. Finally, a labeling is obtained by maximizing the a posteriori energy of the MRFs. Under such formulation, we integrate two different kinds of techniques in an elegant way to make the foreground detection more accurate. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

Exploiting multiple cues in motion segmentation based on background subtraction

“…The sequences segmented are Hermes Outdoor Cam1 from the HERMES database [15,17,34,35,20,52,53,50,25] have been used for performance comparison.…”

“…11 shows frames from Hall Monitor sequence (top row) comparing Wang et al approach [52] (second row), Huang et al approach [53] (third row) and our approach (bottom row). The sequence exhibits challenging Figure 11: First row shows the original frames from the Hall Monitor sequence of the NEMESIS dataset, second row shows the detection results of Wang et al [52], and third row shows the detection results from Huang et al [53]. These images have been obtained directly from [53].…”

“…The sequence exhibits challenging Figure 11: First row shows the original frames from the Hall Monitor sequence of the NEMESIS dataset, second row shows the detection results of Wang et al [52], and third row shows the detection results from Huang et al [53]. These images have been obtained directly from [53]. Fourth row shows the segmentation results of our proposed approach without applying any morphological operation.…”

Background Subtraction in Dynamic Environment based on Modified Adaptive GMM with TTD for Moving Object Detection

“…A simple method was proposed to represent the gray level or color intensity of each pixel in the image as an independent and unimodal distribution [7][8][9][10]. Hung et al proposed a region based background modeling using partial directed Hausdorff distance and MRFs [11,12]. Zivkovic et al proposed equations to constantly update the parameters and selected an appropriate number of components for each pixel [13].…”

Background Subtraction on Embedded Hardware

“…In [66], Markov random fields are used to re-label pixels. First, a region-based motion segmentation algorithm is developed to obtain a set coherent regions.…”