Background subtraction is a key step in a wide spectrum of video applications, such as object tracking and human behavior analysis. Compressive sensing-based methods, which make little specific assumptions about the background, have recently attracted wide attention in background subtraction. Within the framework of compressive sensing, background subtraction is solved as a decomposition and optimization problem, where the foreground is typically modeled as pixel-wised sparse outliers. However, in real videos, foreground pixels are often not randomly distributed, but instead, group clustered. Moreover, due to costly computational expenses, most compressive sensing-based methods are unable to process frames online. In this paper, we take into account the group properties of foreground signals in both spatial and temporal domains, and propose a greedy pursuit-based method called spatio-temporal group sparsity recovery, which prunes data residues in an iterative process, according to both sparsity and group clustering priors, rather than merely sparsity. Furthermore, a random strategy for background dictionary learning is used to handle complex background variations, while foreground-free training is not required. Finally, we propose a two-pass framework to achieve online processing. The proposed method is validated on multiple challenging video sequences. Experiments demonstrate that our approach effectively works on a wide range of complex scenarios and achieves a state-of-the-art performance with far fewer computations.

Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.

In this paper we describe the first version of our system for estimating 3D shape sequences from images of the frontal face. This approach is developed with 3D Visual Speech Animation (VSA) as the target application. In particular, the focus is on the usability of an existing state-of-the-art image-based VSA system and subsequent on-line estimation of the corresponding 3D facial shape sequence from its output. This has the added advantage of a 3D visual speech, which is mainly render ability of the face in different poses and illumination conditions. The idea is based on the detection of landmarks from the facial image which are then used to determine the pose and shape. The method belongs to the category of methods which use a prior 3D Morph able Models (3D-MM) trained using 3D facial data. For the time being it is developed for a person-specific domain, i.e. the 3D-MM and the 2D facial landmark detector are trained using the data of a single person and tested with the same person-specific data.

The problem of visual speech recognition involves the decoding of the video dynamics of a talking mouth in a high-dimensional visual space. In this paper, we propose a generative latent variable model to provide a compact representation of visual speech data. The model uses latent variables to separately represent the interspeaker variations of visual appearances and those caused by uttering within images, and incorporates the structural information of the visual data through placing priors of the latent variables along a curve embedded within a path graph.

Video texture synthesis is the process of providing a continuous and infinitely varying stream of frames, which plays an important role in computer vision and graphics. However, it still remains a challenging problem to generate high-quality synthesis results. Considering the two key factors that affect the synthesis performance, frame representation and blending artifacts, we improve the synthesis performance from two aspects: 1) Effective frame representation is designed to capture both the image appearance information in spatial domain and the longitudinal information in temporal domain. 2) Artifacts that degrade the synthesis quality are significantly suppressed on the basis of a diffeomorphic growth model. The proposed video texture synthesis approach has two major stages: video stitching stage and transition smoothing stage. In the first stage, a video texture synthesis model is proposed to generate an infinite video flow. To find similar frames for stitching video clips, we present a new spatial-temporal descriptor to provide an effective representation for different types of dynamic textures. In the second stage, a smoothing method is proposed to improve synthesis quality, especially in the aspect of temporal continuity. It aims to establish a diffeomorphic growth model to emulate local dynamics around stitched frames. The proposed approach is thoroughly tested on public databases and videos from the Internet, and is evaluated in both qualitative and quantitative ways.

An image-based visual speech animation system is presented in this paper. A video model is proposed to preserve the video dynamics of a talking face. The model represents a video sequence by a low-dimensional continuous curve embedded in a path graph and establishes a map from the curve to the image domain. When selecting video segments for synthesis, we loosen the traditional requirement of using triphone as the unit to allow segments to contain longer natural talking motion. Dense videos are sampled from the segments, concatenated, and downsampled to train a video model that enables efficient time alignment and motion smoothing for the final video synthesis. Different viseme definitions are used to investigate the impact of visemes on the video realism of the animated talking face. The system is built on a public database and tested both objectively and subjectively.

A practical lipreading system can be considered either as subject dependent (SD) or subject-independent (SI). An SD system is user-specific, i.e., customized for some particular user while an SI system has to cope with a large number of users. These two types of systems pose variant challenges and have to be treated differently. In this paper, we propose a simple deterministic model to tackle the problem. The model first seeks a low-dimensional manifold where visual features extracted from the frames of a video can be projected onto a continuous deterministic curve embedded in a path graph. Moreover, it can map arbitrary points on the curve back into the image space, making it suitable for temporal interpolation. Based on the model, we develop two separate strategies for SD and SI lipreading. The former is turned into a simple curve-matching problem while for the latter, we propose a video-normalization scheme to improve the system developed by Zhao et al. We evaluated our system on the OuluVS database and achieved recognition rates more than 20% higher than the ones reported by Zhao et al. in both SD and SI testing scenarios.

In this paper, we propose a novel graph embedding method for the problem of lipreading. To characterize the temporal connections among video frames of the same utterance, a new distance metric is defined on a pair of frames and graphs are constructed to represent the video dynamics based on the distances between frames. Audio information is used to assist in calculating such distances. For each utterance, a subspace of the visual feature space is learned from a well-defined intrinsic and penalty graph within a graph-embedding framework. Video dynamics are found to be well preserved along some dimensions of the subspace. Discriminatory cues are then decoded from curves of the projected visual features to classify different utterances.