Waterfront surveillance and trackability

Authors:
Yi Li;Wei Hua;Chengen Guo;Haisong Gu;Jinman Kang;Xiangrong Chen
Affiliations:
Vidient Systems Inc., 4000 Burton Drive, 95054, Santa Clara, CA, USA and Microsoft, Microsoft Way, 4000 Burton Drive, 98052, Redmond, WA, USA;Vidient Systems Inc., 4000 Burton Drive, 95054, Santa Clara, CA, USA;Vidient Systems Inc., 4000 Burton Drive, 95054, Santa Clara, CA, USA and Optovue Inc., 45531 Northport Loop, 94538, W. Fremont, CA, USA;Vidient Systems Inc., 4000 Burton Drive, 95054, Santa Clara, CA, USA;Vidient Systems Inc., 4000 Burton Drive, 95054, Santa Clara, CA, USA;Vidient Systems Inc., 4000 Burton Drive, 95054, Santa Clara, CA, USA and Optovue Inc., 45531 Northport Loop, 94538, W. Fremont, CA, USA
Venue:
Machine Vision and Applications
Year:
2008

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Abstract

This paper presents a method for waterfront surveillance system. Unlike traditional approaches that model dynamic water background explicitly, we choose a relaxed background model to extract multiple object hypotheses. The hypotheses are then tracked with probablistic framework. Finally, the hypotheses are classified as positive objects or negative objects based on their trackability. Trackability is described by the stableness and the consistency of their trajectories and their appearances, and the properties of their accumulated templates.