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

Liu, Zhi; Shen, Liquan; Zhengkang, Han; Zhang, Zhaoyang

doi:10.1109/icip.2007.4379324

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…They can be classified roughly into two categories: semiautomatic and automatic methods. Semiautomatic methods [10][11][12][13] first identify regions of interest coarsely using initial user interactions. Then, based on the initial information, they construct color, position, or motion models of objects and the background.…”

Section: Introductionmentioning

confidence: 99%

“…Their algorithm achieves high quality video segmentation in realtime, but it works only if ground truth data is available for training model parameters. Also, tracking-based algorithms have been proposed in [12,13]. They extract objects in the first frame based on 2 EURASIP Journal on Advances in Signal Processing users' markings, and then track the objects in subsequent frames using color, position, and temporal cues.…”

Section: Introductionmentioning

confidence: 99%

“…They extract objects in the first frame based on 2 EURASIP Journal on Advances in Signal Processing users' markings, and then track the objects in subsequent frames using color, position, and temporal cues. These semiautomatic methods [10][11][12][13] can achieve relatively accurate segmentation results using initial interactions. However, the interactions prevent them from being used in applications in which full automation is required.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Moving Object Segmentation from Video Sequences Using Alternate Flashing System

Ahn

Lee

2010

EURASIP J. Adv. Signal Process.

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A novel algorithm to extract moving objects from video sequences is proposed in this paper. The proposed algorithm employs a flashing system to obtain an alternate series of lit and unlit frames from a single camera. For each unlit frame, the proposed algorithm synthesizes the corresponding lit frame using a motion-compensated interpolation scheme. Then, by comparing the unlit frame with the lit frame, we construct the sensitivity map, which provides depth cues. In addition to the sensitivity term, color, coherence, and smoothness terms are employed to define an energy function, which is minimized to yield segmentation results. Moreover, we develop a faster version of the proposed algorithm, which reduces the computational complexity significantly at the cost of slight performance degradation. Experiments on various test sequences show that the proposed algorithm provides high-quality segmentation results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Moving Object Segmentation from Video Sequences Using Alternate Flashing System

Ahn

Lee

2010

EURASIP J. Adv. Signal Process.

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“…Multivariate density estimation is an important tool in computer vision for statistical decision making and pattern recognition tasks (Elgammel et al [2003], Liu et al [2007], Zhang et al [2005] etc.). In density estimation, there are broadly three parallel worlds, parametric, semi-parametric and nonparametric, with their own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the computational efficiency of modern day systems has also increased. As a result, the nonparametric KDE is gaining popularity in different image and video processing applications (Elgammel et al [2003], Liu et al [2007], Zhang et al [2005]).…”

Section: Introductionmentioning

confidence: 99%

Fast Estimation of Nonparametric Kernel Density Through PDDP, and its Application in Texture Synthesis

Sinha

Gupta

2008

Electronic Workshops in Computing

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In this work, a new algorithm is proposed for fast estimation of nonparametric multivariate kernel density, based on principal direction divisive partitioning (PDDP) of the data space.The goal of the proposed algorithm is to use the finite support property of kernels for fast estimation of density. Compared to earlier approaches, this work explains the need of using boundaries (for partitioning the space) instead of centroids (used in earlier approaches), for better unsupervised nature (less user incorporation), and lesser (or atleast same) computational complexity. In earlier approaches, the finite support of a fixed kernel varies within the space due to the use of cluster centroids. It has been argued that if one uses boundaries (for partitioning) rather than centroids, the finite support of a fixed kernel does not change for a constant precision error. This fact introduces better unsupervision within the estimation framework. The main contribution of this work is the insight gained in the kernel density estimation with the incorporation of clustering algortihm and its application in texture synthesis. Texture synthesis through nonparametric, noncausal, Markov random field (MRF), has been implemented earlier through estimation of and sampling from nonparametric conditional density. The incorporation of the proposed kernel density estimation algorithm within the earlier texture synthesis algorithm reduces the computational complexity with perceptually same results. These results provide the efficacy of the proposed algorithm within the context of natural texture synthesis.

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Visual object tracking via adaptive deep feature matching and overlap maximization

Aklak

Vadamala

2023

Pattern Anal Applic

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

Cited by 8 publications

References 11 publications

Automatic Moving Object Segmentation from Video Sequences Using Alternate Flashing System

Automatic Moving Object Segmentation from Video Sequences Using Alternate Flashing System

Fast Estimation of Nonparametric Kernel Density Through PDDP, and its Application in Texture Synthesis

Visual object tracking via adaptive deep feature matching and overlap maximization

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