Object Tracking using Incremental 2D-PCA Learning and ML Eestimation

• Video surveillance has drawn increasing interests in recent years. This paper addresses the issue of moving object tracking from videos. A two-step processing procedure is proposed: an incremental 2DPCA (two-dimensional PrincipalComponent Analysis)-based method for characterizing objectsgiven the tracked regions, and a ML (Maximum Likelihood)blob-tracking process given the object characterizationand the previous blob sequence. The proposed incremental2DPCA updates the row- and column-projected covariancematrices recursively, and is computationally more efficient for online learning of dynamic objects. The proposed ML blobtracking takes into account both the shape information and object characteristics. Tests and evaluations were performed on indoor and outdoor image sequences containing a range of moving objects in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

video surveillance

ML estimation

object tracking

incremental 2DPCA

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