text: The ability to identify the static background in videos captured by a moving camera is an important pre-requisite for many video applications (e.g. video stabilization, stitching, and segmentation). Existing methods usually face difficulties when the foreground objects occupy a larger area than the background in the image. Many methods also cannot scale up to handle densely sampled feature trajectories. In this paper, we propose an efficient local-to-global method to identify background, based on the assumption that as long as there is sufficient camera motion, the cumulative background features will have the largest amount of trajectories. Our motion model at the two-frame level is based on the epipolar geometry so that there will be no over-segmentation problem, another issue that plagues the 2D motion segmentation approach. Foreground objects erroneously labelled due to intermittent motions are also taken care of by checking their global consistency with the final estimated background motion. Lastly, by virtue of its efficiency, our method can deal with densely sampled trajectories. It outperforms several state-of-the-art motion segmentation methods on public datasets, both quantitatively and qualitatively.
  @cite_30: 
  @cite_18: Existing video object cutout systems can only deal with limited cases. They usually require detailed user interactions to segment real-life videos, which often suffer from both inseparable statistics (similar appearance between foreground and background) and temporal discontinuities (e.g. large movements, newly-exposed regions following disocclusion or topology change). In this paper, we present an efficient video cutout system to meet this challenge. A novel directional classifier is proposed to handle temporal discontinuities robustly, and then multiple classifiers are incorporated to cover a variety of cases. The outputs of these classifiers are integrated via another classifier, which is learnt from real examples. The foreground matte is solved by a coherent matting procedure, and remaining errors can be removed easily by additive spatio-temporal local editing. Experiments demonstrate that our system performs more robustly and more intelligently than existing systems in dealing with various input types, thus saving a lot of user labor and time.
  @cite_14: A major challenge in video segmentation is that the foreground object may move quickly in the scene at the same time its appearance and shape evolves over time. While pairwise potentials used in graph-based algorithms help smooth labels between neighboring (super)pixels in space and time, they offer only a myopic view of consistency and can be misled by inter-frame optical flow errors. We propose a higher order supervoxel label consistency potential for semi-supervised foreground segmentation. Given an initial frame with manual annotation for the foreground object, our approach propagates the foreground region through time, leveraging bottom-up supervoxels to guide its estimates towards long-range coherent regions. We validate our approach on three challenging datasets and achieve state-of-the-art results.
  @cite_11: We present a technique for separating foreground objects from the background in a video. Our method is fast, fully automatic, and makes minimal assumptions about the video. This enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object motion and appearance, and non-rigid deformations and articulations. In experiments on two datasets containing over 1400 video shots, our method outperforms a state-of-the-art background subtraction technique [4] as well as methods based on clustering point tracks [6, 18, 19]. Moreover, it performs comparably to recent video object segmentation methods based on object proposals [14, 16, 27], while being orders of magnitude faster.
  @cite_26: We present an approach to discover and segment foreground object(s) in video. Given an unannotated video sequence, the method first identifies object-like regions in any frame according to both static and dynamic cues. We then compute a series of binary partitions among those candidate “key-segments” to discover hypothesis groups with persistent appearance and motion. Finally, using each ranked hypothesis in turn, we estimate a pixel-level object labeling across all frames, where (a) the foreground likelihood depends on both the hypothesis's appearance as well as a novel localization prior based on partial shape matching, and (b) the background likelihood depends on cues pulled from the key-segments' (possibly diverse) surroundings observed across the sequence. Compared to existing methods, our approach automatically focuses on the persistent foreground regions of interest while resisting oversegmentation. We apply our method to challenging benchmark videos, and show competitive or better results than the state-of-the-art.
  @cite_28: This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of hand labelled pixels, typically the start and end frames of a chunk of video, an EM based algorithm propagates labels through the rest of the frames of the video sequence. As a result, the user obtains pixelwise labelled video sequences along with the class probabilities at each pixel. Our novel algorithm provides an essential tool to reduce tedious hand labelling of video sequences, thus producing copious amounts of useable ground truth data. A novel application of this algorithm is in semi-supervised learning of discriminative classifiers for video segmentation and scene parsing. The label propagation scheme can be based on pixel-wise correspondences obtained from motion estimation, image patch based similarities as seen in epitomic models or even the more recent, semantically consistent hierarchical regions. We compare the abilities of each of these variants, both via quantitative and qualitative studies against ground truth data. We then report studies on a state of the art Random forest classifier based video segmentation scheme, trained using fully ground truth data and with data obtained from label propagation. The results of this study strongly support and encourage the use of the proposed label propagation algorithm.
  @cite_29: 
  @cite_6: We address the problem of Foreground Background segmentation of “unconstrained” video. By “unconstrained” we mean that the moving objects and the background scene may be highly non-rigid (e.g., waves in the sea); the camera may undergo a complex motion with 3D parallax; moving objects may suffer from motion blur, large scale and illumination changes, etc. Most existing segmentation methods fail on such unconstrained videos, especially in the presence of highly non-rigid motion and low resolution. We propose a computationally efficient algorithm which is able to produce accurate results on a large variety of unconstrained videos. This is obtained by casting the video segmentation problem as a voting scheme on the graph of similar (‘re-occurring’) regions in the video sequence. We start from crude saliency votes at each pixel, and iteratively correct those votes by ‘consensus voting’ of re-occurring regions across the video sequence. The power of our consensus voting comes from the non-locality of the region re-occurrence, both in space and in time – enabling propagation of diverse and rich information across the entire video sequence. Qualitative and quantitative experiments indicate that our approach outperforms current state-of-the-art methods.
  @cite_24: Manually segmenting and labeling objects in video sequences is quite tedious, yet such annotations are valuable for learning-based approaches to object and activity recognition. While automatic label propagation can help, existing methods simply propagate annotations from arbitrarily selected frames (e.g., the first one) and so may fail to best leverage the human effort invested. We define an active frame selection problem: select k frames for manual labeling, such that automatic pixel-level label propagation can proceed with minimal expected error. We propose a solution that directly ties a joint frame selection criterion to the predicted errors of a flow-based random field propagation model. It selects the set of k frames that together minimize the total mislabeling risk over the entire sequence. We derive an efficient dynamic programming solution to optimize the criterion. Further, we show how to automatically determine how many total frames k should be labeled in order to minimize the total manual effort spent labeling and correcting propagation errors. We demonstrate our method's clear advantages over several baselines, saving hours of human effort per video.
  @cite_2: We present a novel approach to video segmentation using multiple object proposals. The problem is formulated as a minimization of a novel energy function defined over a fully connected graph of object proposals. Our model combines appearance with long-range point tracks, which is key to ensure robustness with respect to fast motion and occlusions over longer video sequences. As opposed to previous approaches based on object proposals, we do not seek the best per-frame object hypotheses to perform the segmentation. Instead, we combine multiple, potentially imperfect proposals to improve overall segmentation accuracy and ensure robustness to outliers. Overall, the basic algorithm consists of three steps. First, we generate a very large number of object proposals for each video frame using existing techniques. Next, we perform an SVM-based pruning step to retain only high quality proposals with sufficiently discriminative power. Finally, we determine the fore-and background classification by solving for the maximum a posteriori of a fully connected conditional random field, defined using our novel energy function. Experimental results on a well established dataset demonstrate that our method compares favorably to several recent state-of-the-art approaches.
  @cite_5: In this paper, we propose a technique for video object segmentation using patch seams across frames. Typically, seams, which are connected paths of low energy, are utilised for retargeting, where the primary aim is to reduce the image size while preserving the salient image contents. Here, we adapt the formulation of seams for temporal label propagation. The energy function associated with the proposed video seams provides temporal linking of patches across frames, to accurately segment the object. The proposed energy function takes into account the similarity of patches along the seam, temporal consistency of motion and spatial coherency of seams. Label propagation is achieved with high fidelity in the critical boundary regions, utilising the proposed patch seams. To achieve this without additional overheads, we curtail the error propagation by formulating boundary regions as rough-sets. The proposed approach out-perform state-of-the-art supervised and unsupervised algorithms, on benchmark datasets.
  @cite_15: 
  @cite_10: We introduce an unsupervised, geodesic distance based, salient video object segmentation method. Unlike traditional methods, our method incorporates saliency as prior for object via the computation of robust geodesic measurement. We consider two discriminative visual features: spatial edges and temporal motion boundaries as indicators of foreground object locations. We first generate framewise spatiotemporal saliency maps using geodesic distance from these indicators. Building on the observation that foreground areas are surrounded by the regions with high spatiotemporal edge values, geodesic distance provides an initial estimation for foreground and background. Then, high-quality saliency results are produced via the geodesic distances to background regions in the subsequent frames. Through the resulting saliency maps, we build global appearance models for foreground and background. By imposing motion continuity, we establish a dynamic location model for each frame. Finally, the spatiotemporal saliency maps, appearance models and dynamic location models are combined into an energy minimization framework to attain both spatially and temporally coherent object segmentation. Extensive quantitative and qualitative experiments on benchmark video dataset demonstrate the superiority of the proposed method over the state-of-the-art algorithms.
  @cite_25: In this paper, we propose a novel approach to extract primary object segments in videos in the object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.
  @cite_12: Occlusion relations inform the partition of the image domain into “objects” but are difficult to determine from a single image or short-baseline video. We show how long-term occlusion relations can be robustly inferred from video, and used within a convex optimization framework to segment the image domain into regions. We highlight the challenges in determining these occluder occluded relations and ensuring regions remain temporally consistent, propose strategies to overcome them, and introduce an efficient numerical scheme to perform the partition directly on the pixel grid, without the need for superpixelization or other preprocessing steps.
  @cite_17: In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatiotemporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.
summary: While the aim of video object segmentation is to segment out the foreground objects from a video sequence, this approach can indeed be used to perform background extraction. However, many of these works require some degree of human intervention, such as the semi-supervised methods @cite_28 @cite_24 @cite_14 @cite_5 @cite_2 @cite_17 which require a small amount of manual annotation, and the fully-supervised methods @cite_18 @cite_15 @cite_30 which require repeating result correction by the user. Our work is completely autonomous, like the unsupervised methods of video object segmentation @cite_6 @cite_29 @cite_11 @cite_10 @cite_12 @cite_25 @cite_26 . However, the underlying assumption of our work is less brittle compared to those subscribed to by these works. For instance, @cite_26 @cite_25 rely on the ability of object proposals to detect the foreground objects; this might fail to work when the foreground objects have complex non-compact shapes. @cite_11 @cite_10 detect foreground objects by analysing the 2D motion field based on a simple assumption that they usually move differently from their surroundings. However, the 2D motions of some background objects may also have this property if the background has large depth variation.