2,305 research outputs found
Study on Segmentation and Global Motion Estimation in Object Tracking Based on Compressed Domain
Object tracking is an interesting and needed procedure for many real time applications. But it is a challenging one, because of the presence of challenging sequences with abrupt motion occlusion, cluttered background and also the camera shake. In many video processing systems, the presence of moving objects limits the accuracy of Global Motion Estimation (GME). On the other hand, the inaccuracy of global motion parameter estimates affects the performance of motion segmentation. In the proposed method, we introduce a procedure for simultaneous object segmentation and GME from block-based motion vector (MV) field, motion vector is refined firstly by spatial and temporal correlation of motion and initial segmentation is produced by using the motion vector difference after global motion estimation
Region-based representations of image and video: segmentation tools for multimedia services
This paper discusses region-based representations of image and video that are useful for multimedia services such as those supported by the MPEG-4 and MPEG-7 standards. Classical tools related to the generation of the region-based representations are discussed. After a description of the main processing steps and the corresponding choices in terms of feature spaces, decision spaces, and decision algorithms, the state of the art in segmentation is reviewed. Mainly tools useful in the context of the MPEG-4 and MPEG-7 standards are discussed. The review is structured around the strategies used by the algorithms (transition based or homogeneity based) and the decision spaces (spatial, spatio-temporal, and temporal). The second part of this paper proposes a partition tree representation of images and introduces a processing strategy that involves a similarity estimation step followed by a partition creation step. This strategy tries to find a compromise between what can be done in a systematic and universal way and what has to be application dependent. It is shown in particular how a single partition tree created with an extremely simple similarity feature can support a large number of segmentation applications: spatial segmentation, motion estimation, region-based coding, semantic object extraction, and region-based retrieval.Peer ReviewedPostprint (published version
MULTI-OBJECT TRACKING USING ST-MRF, GMM, MODIFIED RUNNING AVERAGE AND CAMSHIFT - A COMPARATIVE STUDY
Video-based object tracking in static or in dynamic scenes is one of the challenging problems with vast variety of applications, is currently one of the most active research topics in computer vision. This paper mainly focuses on performing survey on tracking moving objects in video scenes in both pixel-domain and compressed-domain with detailed descriptions of tracking strategies and examining their pros and cons. Survey of tracking methodologies in both pixel and compressed domain for object recognition and tracking includes modified running average, Gaussian Mixture Model, Spatial-temporal MRF and Camshift. Experimental result has been evaluated for different video sequences with different conditions such as noise; illumination changes, shadow, scale change in the objects etc. estimate the performance of these algorithms. Result obtained has better accuracy, good performances and with the consumption of less processing time according to the evaluation criteria
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Object detection is a fundamental step for automated video analysis in many
vision applications. Object detection in a video is usually performed by object
detectors or background subtraction techniques. Often, an object detector
requires manually labeled examples to train a binary classifier, while
background subtraction needs a training sequence that contains no objects to
build a background model. To automate the analysis, object detection without a
separate training phase becomes a critical task. People have tried to tackle
this task by using motion information. But existing motion-based methods are
usually limited when coping with complex scenarios such as nonrigid motion and
dynamic background. In this paper, we show that above challenges can be
addressed in a unified framework named DEtecting Contiguous Outliers in the
LOw-rank Representation (DECOLOR). This formulation integrates object detection
and background learning into a single process of optimization, which can be
solved by an alternating algorithm efficiently. We explain the relations
between DECOLOR and other sparsity-based methods. Experiments on both simulated
data and real sequences demonstrate that DECOLOR outperforms the
state-of-the-art approaches and it can work effectively on a wide range of
complex scenarios.Comment: 30 page
Object-based video representations: shape compression and object segmentation
Object-based video representations are considered to be useful for easing the process of multimedia content production and enhancing user interactivity in multimedia productions. Object-based video presents several new technical challenges, however.
Firstly, as with conventional video representations, compression of the video data is a
requirement. For object-based representations, it is necessary to compress the shape of
each video object as it moves in time. This amounts to the compression of moving
binary images. This is achieved by the use of a technique called context-based
arithmetic encoding. The technique is utilised by applying it to rectangular pixel blocks and as such it is consistent with the standard tools of video compression. The blockbased application also facilitates well the exploitation of temporal redundancy in the sequence of binary shapes. For the first time, context-based arithmetic encoding is used in conjunction with motion compensation to provide inter-frame compression. The method, described in this thesis, has been thoroughly tested throughout the MPEG-4 core experiment process and due to favourable results, it has been adopted as part of the MPEG-4 video standard.
The second challenge lies in the acquisition of the video objects. Under normal conditions, a video sequence is captured as a sequence of frames and there is no inherent information about what objects are in the sequence, not to mention information relating to the shape of each object. Some means for segmenting semantic objects from general video sequences is required. For this purpose, several image analysis tools may be of help and in particular, it is believed that video object tracking algorithms will be important. A new tracking algorithm is developed based on piecewise polynomial motion representations and statistical estimation tools, e.g. the expectationmaximisation method and the minimum description length principle
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
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