6533b7d1fe1ef96bd125c3c5
RESEARCH PRODUCT
Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors
Stan SclaroffMarco La CasciaLiliana Lo Prestisubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniMatching (statistics)business.industryComputer scienceNode (networking)Video surveillanceObject matchingObject (computer science)Latent Dirichlet allocationsymbols.namesakeSalientMargin (machine learning)symbolsComputer visionArtificial intelligencebusinessCorrespondence problemconsistent labellingdescription
Establishing correspondences among object instances is still challenging in multi-camera surveillance systems, especially when the cameras’ fields of view are non-overlapping. Spatiotemporal constraints can help in solving the correspondence problem but still leave a wide margin of uncertainty. One way to reduce this uncertainty is to use ap- pearance information about the moving objects in the site. In this paper we present the preliminary results of a new method that can capture salient appearance characteristics at each camera node in the network. A Latent Dirichlet Allocation (LDA) model is created and maintained at each node in the camera network. Each object is encoded in terms of the LDA bag-of-words model for appearance. The encoded appearance is then used to establish probable matching across cameras. Preliminary experiments are conducted on a dataset of 20 individuals and comparison against Madden’s I-MCHR is reported.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-01-01 |