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Large–scale visual sensor networks have become progressively an essential part of our daily lives underpinning many technological, financial, and social advancements today, with applications in smart cities, traffic monitoring, environmental pollution control, public safety, and crime prevention.

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In the above article [1] , the title is incorrect. The correct title is “End-to-End Learning Deep CRF Models for Multi-Object Tracking.”

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This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name

Visual content analysis and understanding attract tremendous attention because of its potentially wide range of applications including human activity analysis, automated photo face tagging, multicamera tracking, crowded counting, and biometric security. With recent progress in end-to-end differentiable learning, the accuracy of algorithms has been significantly improved and even outperforms humans

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Although video enhancement and evaluation have been studied for many years, they are still challenging due to the evolutions of video acquiring and processing techniques. While the development of deep learning is undoubtedly exciting and has demonstrated its superior performance in a variety of applications, it is important to further investigate advanced technologies and solutions to bring seemingly

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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In Recent years, with the vast development of deep learning techniques, a great deal of effort has been devoted in the computer vision and multimedia community toward the problems of visual object analysis, such as object representation, recognition, detection, identification, etc. Especially at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, the computers have successfully

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Representation learning methods allow a system to automatically learn robust and discriminative features from raw data for given goals, which play an important role in various visual content understanding applications, such as visual object segmentation, detection, tracking, recognition, and search. The performance of visual content understanding tasks is heavily dependent on the choice of data representation

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Kinship verification is an interesting and important problem in the fields of computer vision. In practice, the biggest obstacle in kinship verification is that the representation capability of extracted features may not be powerful due to the significant differences between facial images of family members. To effectively address this problem, we propose a semi-coupled synthesis and analysis dictionary

Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled

It is challenging to extend the support region of state-of-the-art local edge-preserving filtering approaches to the entire input image on account of huge memory cost and heavy computational burden. In this paper, we propose an $O(N)$ time recursive non-local edge-aware filter. A novel graph and a linear time algorithm are presented to effectively propagate information across the entire image. In this

When dealing with semantic segmentation, how to locate the object boundary information more accurately is a key problem to distinguish different objects better. The existing methods lose some image information more or less in the process of feature extraction, which also includes the boundary and context information. At present, some semantic segmentation methods use CRFs (conditional random fields)

As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as immersive telepresence, navigation for autonomous driving and gaming. Nevertheless, the tremendous amount of data in dynamic point clouds significantly burden transmission and storage. To this end, we propose a complete

Quality assessment of omnidirectional images has become increasingly urgent due to the rapid growth of virtual reality applications. Different from traditional 2D images and videos, omnidirectional contents can provide consumers with freely changeable viewports and a larger field of view covering the $360^{\circ }\times 180^{\circ }$ spherical surface, which makes the objective quality assessment of

In this article, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach

With the rapid development of video distribution and broadcasting, affective video content analysis has attracted a lot of research and development activities recently. Predicting emotional responses of movie audiences is a challenging task in affective computing, since the induced emotions can be considered relatively subjective. In this article, we propose a multimodal local-global attention network

Current methods for skeleton-based human action recognition usually work with complete skeletons. However, in real scenarios, it is inevitable to capture incomplete or noisy skeletons, which could significantly deteriorate the performance of current methods when some informative joints are occluded or disturbed. To improve the robustness of action recognition models, a multi-stream graph convolutional

Broadcasters are converting their live program production facilities to Internet Protocol (IP)-networks. The introduction of IP-networks has many possibilities such as cost reduction and remote production, and various efforts are underway. At present, the main issue is to consider the construction of equipment for 2K high definition television (HDTV), and it is expected that the introduction of higher

Video question generation is a challenging task in visual information retrieval, which generates questions given a sequence of video frames. The existing methods mainly tackle the problem of single-turn video question generation, but single-turn conversation usually can’t meet the needs of video information acquisition. In this paper, we propose a new framework for single-turn VQG, which introduces

The drastic visual improvements introduced by High Dynamic Range (HDR) technologies open new markets for a wide range of industries. Among them, a significant opportunity is offered to owners of legacy Standard Dynamic Range (SDR) content that can be converted to the new standard to take advantage of the enhanced capabilities of the HDR displays. Similarly, since SDR broadcasting infrastructure will

While many RGB-based saliency detection algorithms have recently shown the capability of segmenting salient objects from an image, they still suffer from unsatisfactory performance when dealing with complex scenarios, insufficient illumination or occluded appearances. To overcome this problem, this article studies RGB-T saliency detection, where we take advantage of thermal modality’s robustness against

Understanding human-contextual interaction to predict human trajectories is a challenging problem. Most of previous trajectory prediction approaches focused on modeling the human-human interaction located in a near neighborhood and neglected the influence of individuals which are farther in the scene as well as the scene layout. To alleviate these limitations, in this article we propose a model to

Dense video captioning aims to localize and describe multiple events in untrimmed videos, which is a challenging task that draws attention recently in computer vision. Although existing methods have achieved impressive performance, most of them only focus on local information of event segments or very simple event-level context, overlooking the complexity of event-event relationship and the holistic

Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval (FG-SBVR), where a sketch sequence is used as a query to retrieve a specific target video instance. Compared with sketch-based still image retrieval, and coarse-grained category-level video retrieval

Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution

The rapid development and growing of 4G and 5G mobile networks allow people all over the world to efficiently transmit and share data and information while bringing an increasing demand on information security. Data hiding is a general technique to embed secret messages to be protected in an imperceptible way into a cover media like an image, a video stream, or a document. Traditional data hiding intends

In this paper, we address a problem of view-disturbing raindrop removal on a single image. In existing methods to tackle this problem, machine learning based ones seem promising but require elaborate pairwise images, i.e., the raindrop-degraded image and the corresponding clean image of the same scene, for training. To overcome this drawback, we propose a weakly supervised learning based model in the

The past few years in the fields of Person Re-Identification (RE-ID) have seen attention mechanism receives enormous interest as it has superior performance in obtaining discriminative feature representations. However, a wide range of state-of-the-art RE-ID attention models only focus on one-dimensional attention design method, e.g. spatial attention and channels attention, hence the produced attention

Disparity estimation is a popular topic in computer vision and has drawn increasing attention in recent years. In this article, we propose a new multi-stage network for the purpose of two to three-dimensional video conversion that contains two training stages: an initial disparity estimation as the first training stage and depth-image-based rendering (DIBR) as an extra component to form the second

Transfer learning aims to learn an effective classifier for unlabeled target data by borrowing knowledge from well-labeled source data. However, most existing work has emphasized on learning domain invariant features to reduce the distribution discrepancy, which may suffer from the negative transfer problem caused by structure inconsistencies or distribution outliers. To address this challenge, in

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the factors that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be

Being able to predict the salient object is of fundamental importance in image processing and computer vision. With numerous approaches proposed for automatic image and video salient object detection, much less work has been dedicated to detecting and segmenting salient objects from light fields. In this article, based on the intrinsic characteristics of light fields, we carefully explore the complementary

Accelerating the inference of CNNs is critical to their deployment in real-world applications. Among all pruning approaches, the methods of implementing a sparsity learning framework have shown effectiveness as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in

A reliable and accurate visual object 6DoF pose tracking system for texture-less objects plays an important role in various fields of modern industry hence it has been a hot research topic for decades. Traditional feature-based pose tracking methods require rich feature points on objects, and it cannot handle texture-less objects. To tackle this problem, the paper proposes a novel edge-based method

Fractional interpolation has been extensively utilized in a series of video coding standards to generate fractional precision prediction to remove the temporal redundancy in consecutive frames. In the traditional interpolation filter based methods, the fractional samples are interpolated through a linear combination of the neighboring integer samples. This method is simple yet unable to accurately

Recently, Li et al. proposed an effective non-additive distortion model for JPEG steganography by preserving Block Boundary Continuity (BBC) in the spatial domain. However, the method based on BBC only explored how the modifications of DCT coefficient pairs at the same mode in adjacent blocks will impact on BBC. In this paper, we propose a method to enhance the BBC, called BBC++, by considering the

In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image. Specifically, we demonstrate improvements over prior approaches utilizing this framework by introducing: (a) wavelet transform pre-processing for decomposing image into different frequencies for their separate processing

Image editing is an effective solution to adapt contents for different applications. In this paper, we present a roundness-preserving warping model for stereoscopic image editing, in which energy constraints from image quality energy, aesthetics energy and depth adaptation energy are involved in the framework to solve the optimization. Specifically, to preserve object roundness during warping, the

Semi-supervised video object segmentation is a task of propagating instance masks given in the first frame to the entire video. It is a challenging task since it usually suffers from heavy occlusions, large deformation, and large variations of objects. To alleviate these problems, many existing works apply time-consuming techniques such as fine-tuning, post-processing, or extracting optical flow, which

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become

Traditional watermarking algorithms have been extensively studied. As an important type of watermarking schemes, template-based approaches maintain a very high embedding rate. In such scheme, the message is often represented by some dedicatedly designed templates, and then the message embedding process is carried out by additive operation with the templates and the host image. To resist potential distortions

A small ISO and a small exposure time are usually used to capture an image in back- or low-light condition which results in an image with negligible motion blur and small noise but looks dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure

Hazy images suffer from low visibility since the light gets scattered as it passes through various atmospheric particles. Moreover, such images are prone to color distortion, particularly in real weather conditions like sandstorms. In this letter, an effective dehazing technique is proposed using weighted least squares filtering on dark channel prior and color correction that involves automatic detection

Low level image restoration is an integral component of modern artificial intelligence (AI) driven camera pipelines. Most of these frameworks are based on deep neural networks which present a massive computational overhead on resource constrained platform like a mobile phone. In this paper, we propose several lightweight low-level modules which can be used to create a computationally low cost variant