Implementation of Real-Time Vehicle Tracking in City-Scale Video Network. Issue 4 (18th May 2016)

• Record Type: Journal Article
• Authors: Pu, Fangling; Xie, Wentao; Cheng, Yao; Xu, Xin
• Is Part Of: Cybernetics and systems. Volume 47:Issue 4(2016)
• Page Start: 249
• Page End: 260
• Publication Date: 2016-05-18
• Subjects: Gaussian mixture model -- geospatial and temporal connection model -- video surveillance network -- vehicle tracking; Cybernetics -- Periodicals; System theory -- Periodicals; 003.5
• DOI: 10.1080/01969722.2016.1158548
• Languages: English
• ISSNs: 0196-9722

Abstract

ABSTRACT: Tracking unexpected warning vehicles is required for quick response to security incident. To realize real-time vehicle tracking in a large-scale video surveillance network, a geospatial and temporal connection (GSTC) model is introduced to model the connection between videos. The transition time between videos is modeled by a Gaussian mixture model (GMM). With the developed plug-ins based on GSTC and GMM, the video streams of defined geospatial neighbors are automatically called in with the video stream that the object appears in during the tracking process. Experiments show that the ratio of success of real-time tracking is largely increased.