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Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures | | Willie Neiswanger
; Frank Wood
; | | Date: |
11 Oct 2012 | | Abstract: | This paper proposes a technique for the unsupervised detection and tracking
of arbitrary objects in videos. It is intended to reduce the need for detection
and localization methods tailored to specific object types and serve as a
general framework applicable to videos with varied objects, backgrounds, and
image qualities. The technique uses a dependent Dirichlet process mixture
(DDPM) known as the Generalized Polya Urn (GPUDDPM) to model image pixel data
that can be easily and efficiently extracted from the regions in a video that
represent objects. This paper describes a specific implementation of the model
using spatial and color pixel data extracted via frame differencing and gives
two algorithms for performing inference in the model to accomplish detection
and tracking. This technique is demonstrated on multiple synthetic and
benchmark video datasets that illustrate its ability to, without modification,
detect and track objects with diverse physical characteristics moving over
non-uniform backgrounds and through occlusion. | | Source: | arXiv, 1210.3288 | | Services: | Forum | Review | PDF | Favorites | |
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