Spatio-Temporal Association Query Algorithm for Massive Video Surveillance Data in Smart Campus

• Published in: IEEE access Vol. 6; pp. 59871 - 59880
• Main Author: Zhang, Jiwei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Big Data; Clustering; Clustering algorithms; Columns (structural); Eigenvalues and eigenfunctions; Indexes; Queries; Smart campus; Surveillance; surveillance video massive data spatio-temporal index spatio-temporal association query; Video data; Video surveillance
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2873780

When using traditional methods to query massive video surveillance data in intelligent campus, there are some problems such as unstable query and inefficient query. Therefore, a query algorithm for intelligent campus video surveillance data based on spatio-temporal correlation is proposed. In this paper, a spatio-temporal association query algorithm for massive video surveillance data in smart campus was proposed. First, the spatio-temporal data clustering algorithm was introduced to cluster the massive data of surveillance video in smart campus. Then, HBase was used as the overall query structure of spatio-temporal association query algorithm based on the clustering results. Through combining the spatio-temporal features and attributed characteristics, a hierarchical record table was generated to construct the spatio-temporal attribute index of queries. According to the index of attribute columns, we can query massive data in many cases. The query condition was determined by Z curve, and the spatio-temporal association query of massive video surveillance data in smart campus was realized. Experimental results showed that when the number of data node was 5, the execution time of the algorithm of this paper was only 1200 s, which was much shorter than the other traditional algorithms. It was proved that the algorithm can maintain spatio-temporal index, improve query efficiency, and enhance query stability.