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Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar...
There arises the needs for fast processing of continuous video data using embedded devices, for example the one needed for UAV aerial photography. In this paper, we proposed a distributed embedded platform built with NVIDIA Jetson TX1 using deep learning techniques for real time video processing, mainly for object detection. We design a Storm based distributed real-time computation platform and ran...
Video data has become the largest source of big data. Owing to video data's complexities, velocity, and volume, public security and other surveillance applications require efficient, intelligent runtime video processing. To address these challenges, a proposed framework combines two cloud-computing technologies: Storm stream processing and Hadoop batch processing. It uses deep learning to realize...
A lot of video applications such as traffic jam detection and criminal tracking require quick responses for video processing, which rely on a realtime supporting framework. Compared with CPU processors, GPU acceleration can achieve high performance. However in the context of Cloud Computing, GPU-based jobs consume less CPU resources yet occupy a lot more memories compared to CPU-based jobs, especially...
With the rise of video surveillance applications for analyzing real-time and batching video data in large scales, traditional video processing systems are being challenged due to real-time, intelligence and fault-tolerance demand for networked high resolution and large-scale video processing. These challenges can be further exacerbated by the existing predicaments of multi-platform, multi-format,...
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