Cover Caption: The cover image is based on the Research Article Efficient virtual data search for annotationfree vehicle reidentification by Zhijing Wan et al., https://doi.org/10.1002/int.22829.

Vehicle reidentification (re-ID) is the task of retrieving the same vehicle across nonoverlapping cameras, which has made significant progress with the help of abundant manually annotated real images. To avoid the time-consuming and tedious labeling of real images, virtual data sets with large-scale synthetic images have recently been constructed to perform annotation-free model training. However, current methods fail to exploit the potential of virtual data search, that is, searching valuable and representative virtual subdata set for efficient training. This paper presents a novel data sampling strategy from both semantic and feature levels to perform an effective data search. The semantic level determines the sample number of each vehicle identity via the consistency constraint of attribute distribution for source domain and target domain; while the feature level searches valuable and representative samples of each vehicle identity. To our knowledge, we are among the first attempts to search effective virtual data to perform annotation-free vehicle re-ID. Extensive cross-domain experiments from virtual vehicle re-ID data sets to real vehicle re-ID data sets show that our data sampling strategy can significantly reduce the training data volume and even boost the re-ID performance.

Vehicle re-identification (re-ID) has received more attention in recent years as a significant work, making huge contribution to the intelligent video surveillance. The complex intra-class and inter-class variation of vehicle images bring huge challenges for vehicle re-ID, especially for the similar vehicle re-ID. In this paper we focus on an interesting and challenging problem, vehicle re-ID of the same/similar model. Previous works mainly focus on extracting global features using deep models, ignoring the individual loa-cal regions in vehicle front window, such as decorations and stickers attached to the windshield, that can be more discriminative for vehicle re-ID. Instead of directly embedding these regions to learn their features, we propose a Regional Structure-Aware model (RSA) to learn structure-aware cues with the position distribution of individual local regions in vehicle front window area, constructing a FW structural map space. In this map sapce, deep models are able to learn more robust and discriminative spatial structure-aware features to improve the performance for vehicle re-ID of the same/similar model. We evaluate our method on a large-scale vehicle re-ID dataset Vehicle-1M. The experimental results show that our method can achieve promising performance and outperforms several recent state-of-the-art approaches.

With the rapid development of Unmanned Aerial Vehicle (UAV), the compression of video data captured by UAV has become a growing critical issue. However, most advanced coding schemes, like H.264 and HEVC, are oriented for common videos and thus cannot afford ideal coding efficiency when applied to UAV platform. Considering the characteristics of UAV video, much more improvement could be imposed onto current coding schemes to make full use of UAV's sensor information. In this paper, we exploit long term redundancy existing in the video data captured by cruise UAV. Firstly, we establish a long-term wide-range background set for reference. Then we separate each frame into new-area part and overlapped part. Lastly, we use GPS information of each frame to get reference from background set and compress two parts individually. In the experiments, by comparing to standard HEVC, our method has given more than 20% reduction in bitrate and meanwhile more than 4% gain in PSNR.

Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer, named Structured Decorrelation Constraint (SDC), which is applied to the activations of the hidden layers to prevent overfitting and achieve better generalization. SDC impels the network to learn structured representations by grouping the hidden units and encouraging the units within the same group to have strong connections during the training procedure. Meanwhile, it forces the units in different groups to learn non-redundant representations by minimizing the cross-covariance between them. Compared with Dropout, SDC reduces the co-adaptions between the hidden units in an explicit way. Besides, we propose a novel approach called Reg-Conv that can help SDC to regularize the complex convolutional layers. Experiments on extensive datasets show that SDC significantly reduces overfitting and yields very meaningful improvements on classification performance (on CIFAR-10 6.22% accuracy promotion and on CIFAR-100 9.63% promotion).

In order to promote the development of auto-stereoscopic display, MPEG has proposed multi-view plus depth (MVD) format. The depth video is encoded and transmitted with color video to synthesize virtual views at the receiver side. The existing video coding standards such as H.264/AVC introduces coding artifacts along the depth boundaries, which may seriously affects the synthesized view quality and coding efficiency. Many in-loop depth filters such as joint depth filter have been proposed to remove the artifacts in compressed depth video. However, their performance is unstable and affected by the outliers due to the weighted summation. In this paper, based on the sparse prior characteristic in local region of depth map, we propose a joint weighted sparse representation based median filter to select the most relevant neighboring depth pixel as the output during the filter process. Experimental results show the proposed method is more effective in improving the depth video coding efficiency.

In this paper, we exploit a new type of data redundancy in the multisource surveillance video to reduce the huge gap between the growth rate of the data and the video compression rate. Global redundancy caused by correlated appearances of moving objects in multiple videos consists of model similarity, spatial correlation and temporal consistency. Therefore, we propose a global coding scheme of moving objects to eliminate the global redundancy: a model based object reconstruction is initially employed to reconstruct the objects in the video, then a pose-based residual error prediction is developed to compensate the difference between the real video appearance and the initial reconstruction from model. The experiment with two simulated surveillance videos has proved that the proposed coding scheme can achieve better coding performance than the main profile of HEVC and surveillance profile of IEEE 1857-2013.

Background model can help to improve the compression efficiency for surveillance video coding, but the existing frame-based background model is inefficient in some situations, for example, when a region of background changes frequently or periodically. In this paper, a block-based background model is proposed to solve this problem. We save the background blocks recognized from each reconstructed frame into a buffer, thus the background blocks are collected gradually. At the same time, we compose a new background frame for each frame to be encoded based on the background blocks currently available in the buffer. Compared with the pre-built background frame, the instantly composed background frame often predicts more accurately because of the accumulated information about background. Experimental results show that the proposed model achieves better rate-distortion performance over the existing frame-based model in most cases, while keeping almost the same computation complexity.

The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose anovel pooling method within an unsupervised feature learningframework, named Rich and Robust Feature Pooling (R2FP), to better explore rich and robust representation from sparsefeature maps of the input data. Both local and global poolingstrategies are further considered to instantiate such a methodand intensively studied. The former selects the most conductivefeatures in the sub-region and summarizes the joint distributionof the selected features, while the latter is utilized to extractmultiple resolutions of features and fuse the features witha feature balancing kernel for rich representation. Extensiveexperiments on several image recognition tasks demonstratethe superiority of the proposed techniques.

The multi-view video plus depth (MVD) format has been put forward for the call for proposals in free view video (FVV) and 3DTV. Since representing the 3D scene geometry, depth maps are used for synthesizing virtual views. However, compression artifacts of the depth images always lead to geometry distortions in synthesized views. By exploiting LBP features of the corresponding color samples, we propose a novel local binary pattern (LBP) guided depth filter which enables the local neighborhood samples those are in the same object of the current pixel to be filtering input. In recognition of its ability for describing the object edges, the LBP operator is used to calculate the weighted values of the local depth pixels for the depth-map filter. Furthermore, the filter is incorporated into the framework of H.264/MVC as an in-loop filter. The experimental results demonstrate that the proposed approach offers 0.45dB and 0.66dB average PSNR gains in terms of video rendering quality and depth coding efficiency, as well as significant subjective improvement in rendering views.

Dynamic textures are sequences of frames of moving scenes that exhibit certain stationary properties in time, which have great significance in applications such as video production, virtual simulation and virtual walkthroughs. This paper presents an algorithm for dynamic texture extrapolation using for H.264 decoding system. The synthesized frames can be used by the decoder for whole frames loss error concealment. The simulation results show that the proposed whole frames loss error concealment algorithm achieves significant improvement over the motion vector extrapolation method.

Scalable video coding (SVC) is considered as a potentially promising solution to enable the adaptability of video to heterogonous networks and various devices. In spatially scalable video encoder, how to resize the captured high-resolution video to get low-resolution video has great effect on the quality of experience (QoE) in the clients receiving low-resolution video. In this paper, we propose a new resizing algorithm called hybrid epitomic resizing (HER), which can make the resized image preserve the same ‘physical’ resolution with original image by the way of utilizing texture similarity inside image and highlight regions of interest while avoiding potential artifacts. For hybrid epitomic resizing, we also design two new inter-layer prediction methods to eliminate the redundancy between adjacent spatial layers instead of conventional inter-layer prediction. Experimental results show that HER can get resized images with perceptually much better quality and the performance of new inter-layer prediction are comparable to that of conventional inter-layer prediction in H.264 SVC.