Video Segmentation Using Fast Marching and Region Growing Algorithms (original) (raw)

An efficient video object segmentation scheme

2002

In this paper, two new algorithms have been proposed for performing video object segmentation. In both algorithms, a new method has been proposed for adaptive threshold estimation for the change detection process. The method is able to provide a reliable estimation of the threshold without knowing explicitly which data corresponds to the moving and non-moving elements in the video sequence. In addition, the use of new specially detected “moving” edgels and also a graph-based edge-linking method that employs both gradient-based and edge-based constraints have also been proposed in order to refine the object segmentation mask. The second algorithm further incorporates an initialisation phase to solve the problems of dis-occluded regions appearing as part of the segmented moving objects. The proposed work has shown a reasonably fast and good video object segmentation technique that operates in real-time and can be applied in applications such as MPEG-4's content-based video coding and home/office/warehouse security. Comparison with one of the MPEG-4 adopted video object segmentation techniques shows that our approach is very significantly faster and yet produces better results.

Accurate video object segmentation through change detection

2002

Abstract We propose an algorithm for the accurate extraction of video objects from color sequences. The semantics defining the video objects is motion, and the extraction algorithm is based on change detection. The color difference between frames is modeled so as to separate the contributions caused by sensor noise and illumination variations from those caused by meaningful objects.

A semi-automatic seeded region growing algorithm for video object localization and tracking

Signal Processing: Image Communication, 2001

This paper describes a semi-automatic method for moving object segmentation and tracking. This method is suitable when a few objects have to be tracked, while the camera moves and fixates on them. The user delineates approximately the initial locations in a selected frame and specifies the depth ordering of the objects to be tracked. First, motion-based segmentation is obtained through an initial application of a region growing algorithm. The partition map is sequentially tracked from frame to frame using motion compensation and location prediction. The segmentation map is obtained by the region growing algorithm. Translational motion is assumed for the moving objects, and local intensity or color average may be used as additional features. A post-processing procedure regularizes the object boundaries over time. r

Change Detection based Real Time Video Object Segmentation

2012

Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and manipulation. The key idea in our paper is to obtain the moving object region which can be set as the possibility foreground, and the other region set as background. An efficient video object segmentation algorithm is proposed based on change detection and background updating that can quickly extract the moving object from video sequence. The change detection is used to analyse temporal information between successive frames to obtain the change region. Then, the combination of frame difference mask and background subtraction mask is adopted to acquire the initial object mask and further solve the uncovered background problem and still object problem. Moreover, the boundary refinement is introduced to overcome the shadow influence and residual background problem. The advantage of change detection based approaches is the low computational load and system complexity enabling real-time applications.

A Change Information Based Fast Algorithm for Video Object Detection and Tracking

IEEE Transactions on Circuits and Systems for Video Technology, 2011

In this paper, we present a novel algorithm for moving object detection and tracking. The proposed algorithm includes two schemes: one for spatio-temporal spatial segmentation and the other for temporal segmentation. A combination of these schemes is used to identify moving objects and to track them. A compound Markov random field (MRF) model is used as the prior image attribute model, which takes care of the spatial distribution of color, temporal color coherence and edge map in the temporal frames to obtain a spatio-temporal spatial segmentation. In this scheme, segmentation is considered as a pixel labeling problem and is solved using the maximum a posteriori probability (MAP) estimation technique. The MRF-MAP framework is computation intensive due to random initialization. To reduce this burden, we propose a change information based heuristic initialization technique. The scheme requires an initially segmented frame. For initial frame segmentation, compound MRF model is used to model attributes and MAP estimate is obtained by a hybrid algorithm [combination of both simulated annealing (SA) and iterative conditional mode (ICM)] that converges fast. For temporal segmentation, instead of using a gray level difference based change detection mask (CDM), we propose a CDM based on label difference of two frames. The proposed scheme resulted in less effect of silhouette. Further, a combination of both spatial and temporal segmentation process is used to detect the moving objects. Results of the proposed spatial segmentation approach are compared with those of JSEG method, and edgeless and edgebased approaches of segmentation. It is noticed that the proposed approach provides a better spatial segmentation compared to the other three methods.

Object based segmentation of video using color, motion and spatial information

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

Video segmentation is different from segmentation of a single image. While several correct solutions may exist for segmenting a single image, there needs to be a consistency among segmentations of each frame for video segmentation. Previous approaches of video segmentation concentrate on motion, or combine motion and color information in a batch fashion. We propose a maximum a posteriori probability (MAP) framework that uses multiple cues, like spatial location, color and motion, for segmentation. We assign weights to color and motion terms, which are adjusted at every pixel, based on a confidence measure of each feature. We also discuss the appropriate modeling of pdfs of each feature of a region. The correct modeling of the spatial pdf imposes temporal consistency among segments in consecutive frames. This approach unifies the strengths of both color segmentation and motion segmentation in one framework, and shows good results on videos that are not suited for either of these approaches.

Motion segmentation and tracking using a seeded region growing method

9Th European Signal Processing Conference, 1998

We describe a user-guided system for the segmentation of an image sequence. A rst segmentation which involves two images of the sequence is presented, as well as the tracking of its result during a number of frames. The segmentation algorithm is a Region Growing algorithm. The main segmentation feature is the motion of the objects presented in the image, which is combined with the information obtained by their intensity or colour {if neccessary. Two \post-processing" techniques are proposed to stabilize the object boundaries over time. A sense of layering is embedded in the whole process.

Video Scene Segmentation : A Novel Method to Determine Objects

Video segmentation plays an important role in the MPEG-4 standard for multimedia applications. Segmentation of videos into their respective foreground and background articulate its importance in Video compression, human-computer interaction, video editing and manipulation etc. Video sequences are converted into frames and processing is done. The key perspective consider in this paper is the moving object detection with noise reduction. The video segmentation is done by detecting the moving objects on each frames and then labeling on it. A hybrid algorithm is proposed that quickly and efficiently extract the moving objects from the video sequence. Background difference is involved so as to acquire the initial object masking and to solve the uncovered background problem in the frames. The combination of noise reduction and background difference will yield the moving object within the video sequences with accuracy. The proposed algorithm is evaluated with varying input video sequences and results are produced. The experimental results show the method defers low computational complexity and better results in real time applications.

Video Segmentation Using Global Motion Estimation and Compensation

IOSR Journal of Computer Engineering, 2016

Emerging multimedia applications demands content-based video processing. Video has to be segmented into objects for content-based processing. A number of video object segmentation algorithms have been proposed such as semiautomatic and automatic. Semiautomatic methods adds burden to users and also not suitable for some applications. Automatic segmentation systems are still a challenge, although they are required by many applications. The proposed work aims at contributing to develop a system to segment video objects automatically from the background given a sequence of video frames. The proposed work will identify and solve different problems in change detection like uncovered background, global motion of background, temporary poses. To improve the resulting change detection masks, a post-processing method will be used. This technique is used to fill open areas inside object regions with uniform intensity. To improve the boundary of segmentation masks, morphological operations will be used.