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A large number of video applications require real-time response. The high-speed video processing then requires a distributed and parallelized framework utilizing all possible computing resources, i.e., both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) at their best. The CPU–GPU collaboration may cause resource imbalance where GPU-based jobs consume less computing resources while...
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...
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,...
Intelligent video surveillance is a challenging issue due to complicated scenes. Based on empirical and experimental explorations, we propose a multi-person tracking-by-detection framework to achieve pedestrian counting at run time. This framework is integrated with a stream based cloud computing paradigm to improve tracking performance. We evaluated our approach which shows improved time performance...
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