Luminate: Linguistic Understanding and Multi-Granularity Interaction for Video Object Segmentation

Video object tracking and segmentation with box annotation

It is a challenging task to extract segmentation mask of a target from a single noisy video, which involves object discovery coupled with segmentation. To solve this challenge, we present a method to jointly discover and segment an object from a noisy video, where the target disappears intermittently throughout the video. Previous methods either only fulfill video object discovery, or video object segmentation presuming the existence of the object in each frame. We argue that jointly conducting the two tasks in a unified way will be beneficial. In other words, video object discovery and video object segmentation tasks can facilitate each other. To validate this hypothesis, we propose a principled probabilistic model, where two dynamic Markov networks are coupled-one for discovery and the other for segmentation. When conducting the Bayesian inference on this model using belief propagation, the bi-directional message passing reveals a clear collaboration between these two inference tasks. We validated our proposed method in five data sets. The first three video data sets, i.e., the SegTrack data set, the YouTube-objects data set, and the Davis data set, are not noisy, where all video frames contain the objects. The two noisy data sets, i.e., the XJTU-Stevens data set, and the Noisy-ViDiSeg data set, newly introduced in this paper, both have many frames that do not contain the objects. When compared with state of the art, it is shown that although our method produces inferior results on video data sets without noisy frames, we are able to obtain better results on video data sets with noisy frames.

Video object segmentation aims to extract different video objects from a video (i.e., a sequence of consecutive images). It has attracted vast interests and substantial research effort for the past decade because it is a prerequisite for visual content retrieval (e.g., MPEG-7 related schemes), object-based compression and coding (e.g., MPEG-4 codecs), object recognition, object tracking, security video surveillance, traffic monitoring for law enforcement, and many other applications. Video object segmentation is a nonstandardized but indispensable component for an MPEG4/7 scheme in order to successfully develop a complete solution. In fact, in order to utilize MPEG-4 object-based video coding, video object segmentation must first be carried out to extract the required video object masks. Video object segmentation is an even more important issue in military applications such as real-time remote missile/vehicle/soldier’s identification and tracking. Other possible applications include home/office/warehouse security where monitoring and recording of intruders/foreign objects, alarming the personnel concerned or/and transmitting the segmented foreground objects via a bandwidth-hungry channel during the appearance of intruders are of particular interest. Thus, it can be seen that fully automatic video object segmentation tool is a very useful tool that has very wide practical applications in our everyday life where it can contribute to improved efficiency, time, manpower, and cost savings.

Object segmentation, detection and tracking in videos is one of the most important task of computer vision. It is necessary in all of the real time deployed surveillance systems. Various unsupervised and semi-supervised video object segmentation techniques have been implemented and shown efficient results. But all of these techniques process all of the frames of a video sequence, which requires a huge training data and results in a large computational time. In this paper, a semi-supervised technique is proposed which segments an object in a video by just processing a single frame of the sequence. In this framework, a fully convolutional network is used to separate the foreground from the image, create the mask of the object and then segments the object with the help of this mask. The foreground separation in a frame is done by using pre-trained network while, training and testing of rest of the network is done using a specified dataset named as DAVIS. The results show that, the proposed framework takes less computational time and has also improved the overall accuracy of video object segmentation by 10% as compared to previous techniques.

This paper examines the problem of segmentation and tracking of video objects for content-based information retrieval. Segmentation and tracking of video objects plays an important role in index creation and user request definition steps. The object is initially selected using a semi-automatic approach. For this purpose, a user-based selection is required to define roughly the object to be tracked. In this paper, we propose two different methods to allow an accurate contour definition from the user selection. The first one is based on an active contour model which progressively refines the selection by fitting the natural edges of the object while the second used a binary partition tree with a marker and propagation approach. The video object is thus tracked by using a hybrid structure alternately combining a hierarchical mesh for the motion estimation between two frames and a multi-resolution active contour model. This contour model is derived directly from the mesh boundaries in order to reposition the snake's nodes onto the natural edges of the object. The object-based segmentation associated with object tracking allows relevant descriptors to be built for a future matching purpose.

The segmentation of objects in video sequences constitutes a prerequisite for numerous applications ranging from computer vision tasks to second-generation video coding.We propose an approach for segmenting video objects based on motion cues. To estimate motion we employ the 3D structure tensor, an operator that provides reliable results by integrating information from a number of consecutive video frames. We present a new hierarchical algorithm, embedding the structure tensor into a multiresolution framework to allow the estimation of large velocities.The motion estimates are included as an external force into a geodesic active contour model, thus stopping the evolving curve at the moving object's boundary. A level set-based implementation allows the simultaneous segmentation of several objects.As an application based on our object segmentation approach we provide a video object classification system. Curvature features of the object contour are matched by means of a curvature scale space technique to a database containing preprocessed views of prototypical objects.We provide encouraging experimental results calculated on synthetic and real-world video sequences to demonstrate the performance of our algorithms.

Researchers from Nanjing University of Information Science and Technology (NUIST) present an attention-modulating network for video object segmentation with an advanced attention modulator to efficiently modulate a segmentation model to focus on a specific object of interest. The group employ a focal loss that distinguishes simple samples from more difficult ones to accelerate the convergence of network training to achieve state-of-the-art segmentation performance. Video object segmentation (VOS) is a fundamental task in computer vision, with important applications in video editing, robotics, and self-driving cars. VOS tasks are mainly categorised into unsupervised and semi-supervised classifications. The former seeks to find and segment the salient targets in the videos completely without supervision, with the algorithm itself deciding what the main segmentation is. The latter aims at segmenting an object instance throughout the entire video sequence given only the object mask on the first frame. This can be observed as a pixel-level object tracking problem. Semi-supervised VOS can be subdivided into single-object segmentation and multi-object segmentation. In the team's Letter, they focus on semi-supervised VOS. Deep learning for VOS has gained attention in the research community in recent years. Existing semi-supervised VOS techniques work by constructing deep networks and fine-tuning a pre-trained classifier on a given ground truth in the first frame during online testing. This online fine-tuning of a classifier during testing has been shown to significantly improve accuracy. Illustrative diagram of the proposed segmentation model and approach. Segmentation results. The team conduct an attention-modulating network for the semi-supervised VOS task. Co-author Kaihua Zhang elaborates on the process: “We designed an efficient visual and spatial attention modulator based on the semantic information of the annotated object in the first frame and the spatial information of predicted object mask in the previous frame, respectively, to fast module the segmentation model to focus on the specific object of interest. Then we design a SCAM architecture which includes a channel attention module and a spatial attention module and inject it into segmentation model to further refine its feature maps. In addition, we construct a feature pyramid attention module to mine context information of different scales to solve the problem of multi-scale segmentation. Most existing methods rely on fine-tuning models using first-frame annotations and are time-consuming, making them unsuitable for most practical applications. To address this issue, the proposed approach developed an attention-modulating network to focus on the appearance of a specific object instance in one single feed-forward pass without fine-tuning. Compared with other methods, this method has achieved state-of-art performance on the DAVIS2017 dataset by using attention-modulators, feature attention pyramid modules and focal loss. In order to overcome a sample imbalance problem, reference was made to focal loss which can accelerate the convergence of network training, thus helping to distinguish between difficult and simple samples. VOS remains challenging due to occlusions, fast motion, deformation, and significant appearance variations over time. This method conducts a visual attention modulator to extract semantic information such as category, color and shape from the first frame. The spatial attention modulator fits the predicted location of object masks in the previous frame as a spatial prior to guide the segmentation network to focus on the regions where that target is most likely to appear in the current frame. To solve the multi-scales of segmentation objects, feature pyramid attention modules mined the context information of different scales, achieving better pixel-level attention for the high-level feature maps. The proposed VOS approach is fast, which facilitates many applications, such as interactive video editing and augmented reality. It may be applied to video understanding models in the short term, and after long-term development, it may be applied to robotics, and self-driving cars. Kaihua Zhang notes on his groups future work: “Experiments show that our algorithm performs erroneous instance segmentation when faced with the challenge of occluding each other between similar objects. To tackle this problem, we will leverage a position-sensitive embedding which is capable of distinguishing the pixels of similar objects. We have also found that solving VOS with multiple instances requires template matching to deal with occlusion and temporal propagation to ensure temporal continuity; otherwise the segmentation instance would be lost. Thus, we will use the re-identification module to retrieve lost instances and take its frame as the starting point and use the mask propagation module to bi-directionally recover the lost instances.” The development of VOS in the next decade will achieve higher precision while meeting real-time application requirements. At present, the cost of manual annotation of pixel-level VOS data sets is too expensive, so cheaper large-scale VOS data sets are expected in the future.

In modern times, video object segmentation has emerged as one of the most imperative and challenging area of research. The principal objective of video object segmentation is to facilitate content-based representation by extracting objects of interest from a series of consecutive video frames. Recently, a number of video object segmentation algorithms have been discussed and unfortunately most existing segmentation algorithms are not adequate and robust enough to process noisy video sequences. Competence of most segmentation techniques is affected by the presence of noise in frames which is a critical issue of edge preservation. This paper presents a novel video object segmentation approach for noisy color video sequences towards effective video retrieval. Initially, the noisy video frames are denoised using a strategy based on an enhanced sparse representation in transform domain. Afterwards, the background is estimated from the denoised frames using the Expectation Maximization (EM) algorithm. Then, the foreground objects i.e.) moving video objects are segmented with the aid of the novel approach presented. The biorthogonal wavelet transform and the L2 norm distance measure are employed in the foreground object segmentation. The experimental results demonstrate the effectiveness of the presented approach in segmenting the video objects from noisy color video sequences.

Many recent methods for semi-supervised Video Object Segmentation (VOS) have achieved good performance by exploiting the annotated first frame via one-shot fine-tuning or mask propagation. However, heavily relying on the first frame may weaken the robustness for VOS, since video objects can show large variations through time. In this work, we propose a Dynamic Identity Propagation Network (DIPNet) that adaptively propagates and accurately segments the video objects over time. To achieve this, DIPNet factors the VOS task at each time step into a dynamic propagation phase and a spatial segmentation phase. The former utilizes a novel identity representation to adaptively propagate objects’ reference information over time, which enhances the robustness to videos’ temporal variations. The segmentation phase uses the propagated information to tackle the object segmentation as an easier static image problem that can be optimized via light-weight fine-tuning on the first frame, thus reducing the computational cost. As a result, by optimizing these two components to complement each other, we can achieve a robust system for VOS. Evaluations on four benchmark datasets show that DIPNet provides state-of-the-art performance with time efficiency.

This paper examines the problem of segmentation and tracking of video objects for a content-based information retrieval context. Our method starts first with an interactive video object selection, then alternately tracks and fits the object of interest as long as possible. A user-based selection is required in order to initialize the process, whereas an active contour model progressively refines the selection by fitting the natural edges of the object. The video object is thus tracked by using a hybrid structure combining a hierarchical mesh for the motion estimation between two frames and a multi-resolution active contour model. This contour model is derived directly from the mesh boundaries in order to reposition the snake's nodes onto the natural edges of the object.

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in Computer Vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. The proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

Multiple existing benchmarks involve tracking and segmenting objects in video e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e.g. $\mathcal{J}\& {\mathcal{F}}$, mAP, sMOTSA). As a result, published works usually target a particular benchmark, and are not easily comparable to each another. We believe that the development of generalized methods that can tackle multiple tasks requires greater cohesion among these research sub-communities. In this paper, we aim to facilitate this by proposing BURST, a dataset which contains thousands of diverse videos with high-quality object masks, and an associated benchmark with six tasks involving object tracking and segmentation in video. All tasks are evaluated using the same data and comparable metrics, which enables researchers to consider them in unison, and hence, more effectively pool knowledge from different methods across different tasks. Additionally, we demonstrate several baselines for all tasks and show that approaches for one task can be applied to another with a quantifiable and explainable performance difference. Dataset annotations are available at: https://github.com/Ali2500/BURST-benchmark.

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

We implement a video object segmentation system that integrates the novel concept of Voronoi Order with existing surface optimization techniques to support the MPEG-4 functionality of object-addressable video content in the form of video objects. The major enabling technology for the MPEG-4 standard are systems that compute video object segmentation, i.e., the extraction of video objects from a given video sequence. Our surface optimization formulation describes the video object segmentation problem in the form of an energy function that integrates many visual processing techniques. By optimizing this surface, we balance visual information against predictions of models with a priori information and extract video objects from a video sequence. Since the global optimization of such an energy function is still an open problem, we use Voronoi Order to decompose our formulation into a tractable optimization via dynamic programming within an iterative framework. In conclusion, we show the results of the system on the MPEG-4 test sequences, introduce a novel objective measure, and compare results against those that are hand-segmented by the MPEG-4 committee.

Significant progress has been made in Video Object Segmentation (VOS), the video object tracking task in its finest level. While the VOS task can be naturally decoupled into image semantic segmentation and video object tracking, significantly much more research effort has been made in segmentation than tracking. In this paper, we introduce "tracking-by-detection" into VOS which can coherently integrate segmentation into tracking, by proposing a new temporal aggregation network and a novel dynamic time-evolving template matching mechanism to achieve significantly improved performance. Notably, our method is entirely online and thus suitable for one-shot learning, and our end-to-end trainable model allows multiple object segmentation in one forward pass. We achieve new state-of-the-art performance on the DAVIS benchmark without complicated bells and whistles in both speed and accuracy, with a speed of 0.14 second per frame and J&F measure of 75.9% respectively.