Towards a High Speed Video Cloud Based on Batch Processing Integrated with Fast Processing

Abstract

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, multi-codec on video itself. The emergence of cloud computing and big data techniques makes it possible to manage complicated intelligent processing for large-scale video data. This paper proposes a general cloud-based video platform that can provide a robust solution to intelligent analytic and storage for video data, which is called ViCiBaF architecture (Video Cloud integrated with Batch processing and Fast processing) architecture. This ViCiBaF architecture is elaborated through an implementation that can effectively handle massive surveillance video data, where real-time analysis, batch processing, distributed storage and cloud services are seamlessly integrated to meet the requirements of intelligent analysis, real time, fault tolerance and massive storage for massive video data. The evaluations show that the proposed approach is efficient.