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Media outlets have undergone a fundamental shift over the last decade as new distribution channels proliferate in an unprecedented manner. Although mobile devices in particular have been experienced rapid adoption among consumers, its broader implication is yet to be understood. In this research, we examine consumer's news consumption behaviors via mobile(More)
In this paper, we propose a multi-stage feature selection algorithm, which focuses on the reduction of redundant features and the improvement of classification performance using feature ranking (FR), correlation analysis (CA) and chaotic binary particle swarm optimization (CBPSO). In the first stage, with the purpose of selecting the most effective features(More)
High-density crowd segmentation is one of the most important components in a wide range of applications in group analysis, but the complexity and variability of the high-density crowd environment make high-density crowd behaviors segmentation facing great challenges. In this paper, we introduce a holistic approach to perform segmentation using the stability(More)
This paper presents a novel method to recognize high density crowd behaviors using micro-behaviors combining with Sparse Representation based on Locally Linear Embedding (named LLE-based Sparse Representation or LLE-SR). We extract micro-behaviors from each frame, respectively named Fountainhead, Bottleneck, Blocking, Lane and Ring/Arch, and construct(More)
The current works about task scheduling with deadline-constraint in homogeneous environment rarely take the differences of Map and Reduce task and data locality into account in the same scheduler. To address this problem, we introduce a scheduling algorithm that Map and Reduce are regarded as two separated stages of scheduling problem in homogeneous(More)
Video surveillance is becoming more and more significant in the detection of abnormal events for public security. As a usual kind of crowd activity mode, the research on the motion analysis of small groups is under increasing attention. This paper presents a particle video-based abnormal behavior detection method of small groups. First, use a particle(More)
In a video surveillance network, it is always required to track and recognize people when they move through the environment. This paper presents a novel re-identification method for multiple-people using feature selection with sparsity. By using the multiple-shot approach, each of appearance models is created in this method. The human body is divided into(More)
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