A Large-scale Distributed Video Parsing and Evaluation Platform
This work addresses scalability problems for visual surveillance systems, but it is incremental as it combines existing big data techniques without introducing new algorithms.
The authors tackled the scalability and error-prone issues in large-scale video analysis for surveillance by developing a distributed platform using Spark Streaming, Kafka, and HDFS, which includes a web interface for user feedback to evaluate system performance.
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.