For many applications such as action detection or robotic interaction, segmenting all moving objects is a crucial first step. While this problem has been well-studied under the formulation of spatiotemporal video segmentation, virtually none of the prior works use learning-based approaches, despite significant advances in single-frame instance segmentation. We propose the first deep-learning based approach for spatio-temporal grouping. Our model extends the state-of-the-art Mask R-CNN architecture to the video domain. It takes a video frame together with its optical flow as input, and passes them through appearance and motion streams respectively. It then combines the motion cues, which provide a bottom-up signal for object detection, with appearance cues that allow capturing the full extent of the object via a joint RPN module. We show state-of-the-art results on the Freiburg Berkeley Motion Segmentation dataset by a wide margin. One potential worry with learning-based methods is that they might overfit to the particular type of objects that they have been trained on. While current recognition systems tend to be limited to a "closed world" of N objects on which they are trained, our model can segment almost anything that moves.
Foreground Background Video Segmentation Several works have focused on the binary version of the video segmentation task, separating all the moving objects object from the background. Early approaches @cite_28 @cite_41 @cite_17 @cite_40 relied on heuristics in the optical flow field, such as closed motion boundaries in @cite_41 to identified moving objects. These initial estimates were then refined with appearance, utilizing external cues, such as saliency maps @cite_17 , or object shape estimates @cite_40 . Another line of work focused on building probabilistic models of moving objects using on optical flow orientations @cite_6 @cite_11 . None of these methods are based on a robust learning framework and thus they do not generalize well to unseen videos. The recent introduction of a standard dataset, DAVIS 2016 @cite_5 , for this task has led to a renewed interest. More recent approaches propose deep models for directly estimating motion masks, as in @cite_3 @cite_29 @cite_13 . These approaches are similar to ours in that they also use a two-stream architecture to separately process motion and appearance, but they are unable to individually segment object instances. Our method separately segments and tracks each individual moving object in a video.
@cite_11 @cite_28 @cite_41 @cite_29 @cite_6 @cite_3 @cite_40 @cite_5 @cite_13 @cite_17
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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. This paper presents an algorithm capable of real-time separation of foreground from background in monocular video sequences. Automatic segmentation of layers from colour contrast or from motion alone is known to be error-prone. Here motion, colour and contrast cues are probabilistically fused together with spatial and temporal priors to infer layers accurately and efficiently. Central to our algorithm is the fact that pixel velocities are not needed, thus removing the need for optical flow estimation, with its tendency to error and computational expense. Instead, an efficient motion vs nonmotion classifier is trained to operate directly and jointly on intensity-change and contrast. Its output is then fused with colour information. The prior on segmentation is represented by a second order, temporal, Hidden Markov Model, together with a spatial MRF favouring coherence except where contrast is high. Finally, accurate layer segmentation and explicit occlusion detection are efficiently achieved by binary graph cut. The segmentation accuracy of the proposed algorithm is quantitatively evaluated with respect to existing groundtruth data and found to be comparable to the accuracy of a state of the art stereo segmentation algorithm. Foreground background segmentation is demonstrated in the application of live background substitution and shown to generate convincingly good quality composite video. In this paper, we propose a new method for detecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works considering only local saliency detection or common pattern discovery, the proposed method integrates the local visual motion saliency extracted from each frame, global appearance consistency throughout the video, and spatiotemporal smoothness constraint on object trajectories. We first identify a temporal coherent salient region throughout the whole video, and then explicitly learn a global appearance model to distinguish the primary object against the background. In order to obtain high-quality saliency estimations from both appearance and motion cues, we propose a novel self-adaptive saliency map fusion method by learning the reliability of saliency maps from labeled data. As a whole, our method can robustly localize and track primary objects in diverse video content, and handle the challenges such as fast object and camera motion, large scale and appearance variation, background clutter, and pose deformation. Moreover, compared with some existing approaches that assume the object is present in all the frames, our approach can naturally handle the case where the object is present only in part of the frames, e.g., the object enters the scene in the middle of the video or leaves the scene before the video ends. We also propose a new video data set containing 51 videos for primary object detection with per-frame ground-truth labeling. Quantitative experiments on several challenging video data sets demonstrate the superiority of our method compared with the recent state of the arts. 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.