Abstract
Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem (HDFS). Also, a Web User Interface is designed in the system, to collect users’ degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grants 61473290, the National High Technology Research and Development Program of China (863 Program) under Grant 2015AA042307, and the international partnership program of Chinese Academy of Sciences, grant No. 173211KYSB20160008.
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Yu, K., Zhou, Y., Li, D., Zhang, Z., Huang, K. (2016). A Large-Scale Distributed Video Parsing and Evaluation Platform. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_5
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DOI: https://doi.org/10.1007/978-981-10-3476-3_5
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