Title

Bayesian Modeling Of Dynamic Scenes For Object Detection

Keywords

Joint domain range; Kernel density estimation; MAP-MRF estimation; Object detection

Abstract

Accurate detection of moving objects is n imp ant precursor to stable tracking or recognition. In this paper, we present an object erection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes. © 2005 IEEE.

Publication Date

11-1-2005

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

27

Issue

11

Number of Pages

1778-1792

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2005.213

Socpus ID

28044439637 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/28044439637

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