A Novel Video Object Tracking Approach Based on Kernel Density Estimation and Markov Random Field

2007 ◽  
Author(s):  
Zhi Liu ◽  
Liquan Shen ◽  
Zhongmin Han ◽  
Zhaoyang Zhang
Keyword(s):  
Random Field ◽  
Object Tracking ◽  
Density Estimation ◽  
Markov Random Field ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Video Object ◽  
Video Object Tracking ◽  
Markov Random

scholarly journals Recurrent Sampling Models for the Helmholtz Machine

1999 ◽  
Vol 11 (3) ◽  
pp. 653-677 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Random Field ◽  
Density Estimation ◽  
Markov Random Field ◽  
Single Layer ◽  
Recent Analysis ◽  
Stochastic Sampling ◽  
Markov Random ◽  
Analysis By Synthesis ◽  
Mutually Independent ◽  
Estimation Models

Many recent analysis-by-synthesis density estimation models of cortical learning and processing have made the crucial simplifying assumption that units within a single layer are mutually independent given the states of units in the layer below or the layer above. In this article, we suggest using either a Markov random field or an alternative stochastic sampling architecture to capture explicitly particular forms of dependence within each layer. We develop the architectures in the context of real and binary Helmholtz machines. Recurrent sampling can be used to capture correlations within layers in the generative or the recognition models, and we also show how these can be combined.

scholarly journals Kernel Density Estimation and Correntropy based Background Modeling and Camera Model Parameter Estimation for Underwater Video Object Detection

2021 ◽  
Author(s):  
Susmita Panda ◽  
Pradipta Kumar Nanda
Keyword(s):  
Object Detection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Background Modeling ◽  
Model Parameters ◽  
Video Object ◽  
Underwater Video ◽  
Camera Model ◽  
Model Learning

Abstract Underwater video object detection is challenging because of the complex background and the movement of the camera. In order to address this, we propose a novel scheme of simultaneously estimating the camera model parameters and detecting the object. The object detection phase includes background modeling and its learning. Background is modeled by the proposed Spatial Kernel Density Estimation (SKDE) model and the model learning happens in the SKDE feature space. Background modeling and its learning is pixel based approach. The model histograms learn the new pixel through its histogram representation. Our learning and classification strategy is different from the Heikkila et al. [17] in the context of similarity measure. We have proposed the correntropy based similarity measure that is used for model learning and pixel classification. The camera model parameters are estimated by 2D optimization method where we have used the corner features of an object at subpixel accuracy level. These subpixel level features are used in the proposed pipelining framework for model parameters estimation. The estimated model parameters are used to transform the input frame, which in turn is used for model learning and classification. The proposed scheme has been tested with underwater video frames from six data sets. The efficacy of the proposed scheme is compared with seven existing schemes and it is found that the proposed scheme exhibits improved performance as compared to the existing methods.

Sign in / Sign up

Export Citation Format

Share Document