The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 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 as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. 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 1 .

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Today, search engine is the most commonly used tool for Web information retrieval, however, its current status is still far from satisfaction. This paper focuses on clustering Web search results in order to help users find relevant Web information more easily and quickly. The main contributions of this paper include the following.(1) The benefits of using key phrases as natural language information features are discussed. An effective and efficient algorithm based on suffix array for key phrase discovery is presented. The efficiency of this method is very high no matter how large the language's alphabet is. (2) The concept of orthogonal clustering is proposed for general clustering problems. The reason why matrix SVD (Singular Value Decomposition) can provide solution to orthogonal clustering is strictly proved. The orthogonal clustering algorithm has a solid mathematics foundation and many advantages over traditional heuristic clustering algorithms. (3) The WICE system is designed and implemented to automatically organize multilingual Web search results through a semantic, hierarchical, online clustering approach named SHOC.

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