The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

In this paper, we present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of stateof- the-art dehazing algorithms, and suggest promising future directions.

The huge success of the Internet allows for the transmission, wide distribution, and access of electronic data in an effortless manner. Content providers are faced with the challenge of how to protect their electronic data. This problem has generated a flurry of recent research activity in the area of digital watermarking of electronic content for copyright protection. Unlike the traditional visible watermark found on paper, the challenge here is to introduce a digital watermark that does not alter the perceived quality of the electronic content, while being extremely robust to attack. For instance, in the case of image data, editing the picture or illegal tampering should not destroy or transform the watermark into another valid signature. Equally important, the watermark should not alter the perceived visual quality of the image. From a signal processing perspective, the two basic requirements for an effective watermarking scheme, robustness and transparency, conflict with each other. We propose two watermarking techniques for digital images that are based on utilizing visual models which have been developed in the context of image compression. Specifically, we propose watermarking schemes where visual models are used to determine image dependent upper bounds on watermark insertion. This allows us to provide the maximum strength transparent watermark which, in turn, is extremely robust to common image processing and editing such as JPEG compression, rescaling, and cropping. We propose perceptually based watermarking schemes in two frameworks: the block-based discrete cosine transform and multiresolution wavelet framework and discuss the merits of each one. Our schemes are shown to provide very good results both in terms of image transparency and robustness.

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Skeleton-based human action recognition has recently attracted increasing attention due to the popularity of 3D skeleton data. One main challenge lies in the large view variations in captured human actions. We propose a novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action. Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end. Extensive experiment analyses show that the proposed view adaptive RNN model strives to (1) transform the skeletons of various views to much more consistent viewpoints and (2) maintain the continuity of the action rather than transforming every frame to the same position with the same body orientation. Our model achieves significant improvement over the state-of-the-art approaches on three benchmark datasets.