A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.
In the first two steps of our proposed approach, we apply distribution-based outlier detection to detect outlier points by considering only non-spatial attributes and then leverage fast CCL to construct the region outliers by taking into account spatial-attributes. We choose distribution-based outlier detection since it can solve the problem of finding outliers efficiently if an accurate approximation of a data distribution can be properly found @cite_8 @cite_39 . Normally the distribution of the stream data may change over time @cite_22 . However, this assumption may not hold in fusion experiments since a fusion experiment lasts very short time period from a few seconds to hundreds of seconds. Therefore, we consider the simpler problem of fixed distribution parameters, noting that several fusion devices have shown similar distribution functions of blob events. Then we can perform exploratory data analysis to compute best fitted distribution parameters offline and then build an accurate online distribution model. We leave the more complicated problem of real-time distribution estimation for future work.
@cite_22 @cite_39 @cite_8
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We tackle the problem of extracting stay regions from a geospatial trajectory where a user has stayed longer than a certain time threshold. There are four major difficulties with this problem: (1) stay regions are not only point-type ones such as at a bus-stop but large and arbitrary-shaped ones such as at a shopping mall; (2) trajectories contain spatial outliers; (3) there are missing points in trajectories; and (4) trajectories should be analyzed in an online mode. Previous algorithms cannot overcome these difficulties simultaneously. Density-based batch algorithms have advantages over the previous algorithms in discovering of arbitrary-shaped clusters from spatial data containing outliers; however, they do not consider temporal durations and thus have not been used for extracting stay regions. We extended a density-based algorithm so that it would work in a duration-based manner online and have robustness to missing points in stay regions while keeping its advantages. Experiments on real trajectories of 13 users conducting their daily activities for three weeks demonstrated that our algorithm statistically significantly outperformed five state-of-the-art algorithms in terms of F1 score and works well without trajectory preprocessing consisting of filtering, interpolating, and smoothing. Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data. Information from multiple heterogenous data sources (e.g. visible and infrared) or representations (e.g. intensity and edge) have become increasingly important in many video-based applications. Fusion of information from these sources is critical to improve the robustness of related visual information processing systems. In this paper we propose a data fusion approach via sparse representation with applications to robust visual tracking. Specifically, the image patches from different sources of each target candidate are concatenated into a one-dimensional vector that is then sparsely represented in the target template space. The template space representation, which naturally fuses information from different sources, brings several benefits to visual tracking. First, it inherits robustness to appearance contaminations from the previously proposed sparse trackers. Second, it provides a flexible framework that can easily integrate information from different data sources. Third, it can be used for handling various number of data sources, which is very useful for situations where the data inputs arrive at different frequencies. The sparsity in the representation is achieved by solving an l1-regularized least squares problem. The tracking result is then determined by finding the candidate with the smallest approximation error. To propagate the results over time, the sparse solution is combined with the Bayesian state inference framework using the particle filter algorithm. We conducted experiments on several real videos with heterogeneous information sources. The results show that the proposed approach can track the target more robustly than several state-of-the-art tracking algorithms. We propose a novel keypoint-based method for long-term model-free object tracking in a combined matching-and-tracking framework. In order to localise the object in every frame, each keypoint casts votes for the object center. As erroneous keypoints are hard to avoid, we employ a novel consensus-based scheme for outlier detection in the voting behaviour. To make this approach computationally feasible, we propose not to employ an accumulator space for votes, but rather to cluster votes directly in the image space. By transforming votes based on the current keypoint constellation, we account for changes of the object in scale and rotation. In contrast to competing approaches, we refrain from updating the appearance information, thus avoiding the danger of making errors. The use of fast keypoint detectors and binary descriptors allows for our implementation to run in real-time. We demonstrate experimentally on a diverse dataset that is as large as 60 sequences that our method outperforms the state-of-the-art when high accuracy is required and visualise these results by employing a variant of success plots.