Visual contour tracking based on particle filters

Li, Peihua; Zhang, Tianwen; Pece, Arthur E. C.

doi:10.1016/s0262-8856(02)00133-6

Cited by 125 publications

(74 citation statements)

References 18 publications

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“…When the relationship between state and measurement can be linearized, an alternative is to sample from the Gaussian estimate computed by an Extended Kalman Filter associated to each particle [19]. Similarly, EKF can be substituted with an unscented transform that does not require linearization [19,27]. Both methods assume that the modes of the pdf are well represented by their first and second order moments.…”

Section: Introductionmentioning

confidence: 99%

Accurate appearance-based Bayesian tracking for maneuvering targets

Maggio

Cavallaro

2009

Computer Vision and Image Understanding

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We propose a tracking algorithm that combines the Mean Shift search in a Particle Filtering framework and a target representation that uses multiple semi-overlapping color histograms. The target representation introduces spatial information that accounts for rotation and anisotropic scaling without compromising the flexibility typical of color histograms. Moreover, the proposed tracker can generate a smaller number of samples than Particle Filter as it increases the particle efficiency by moving the samples toward close local maxima of the likelihood using Mean Shift. Experimental results show that the proposed representation improves the robustness to clutter and that, especially on highly maneuvering targets, the combined tracker outperforms Particle Filter and Mean Shift in terms of accuracy in estimating the target size and position while generating only 25% of the samples used by Particle Filter.

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Section: Introductionmentioning

confidence: 99%

Accurate appearance-based Bayesian tracking for maneuvering targets

Maggio

Cavallaro

2009

Computer Vision and Image Understanding

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“…Generally speaking, there exist three broad categories of object models in the context of tracking: contour-based models [1], [5], [8], [9], region-based models [2], [3], [4], and feature point-based models [10], [12]. The contour-based model does not encode any color or edge information within the interior of the object.…”

Section: Introductionmentioning

confidence: 99%

A Novel Tracking Framework Using Kalman Filtering and Elastic Matching

Luo

Bhandarkar

2005

Lecture Notes in Computer Science

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Abstract. A novel region-based multiple object tracking framework based on Kalman filtering and elastic matching is proposed. The proposed Kalman filtering-elastic matching model is general in two significant ways. First, it is suitable for tracking of both, rigid and elastic objects. Second, it is suitable for tracking using both, fixed cameras and moving cameras since the method does not rely on background subtraction. The elastic matching algorithm exploits both the spectral features and structural features of the tracked objects, making it more robust and general in the context of object tracking. The proposed tracking framework can be viewed as a generalized Kalman filter where the elastic matching algorithm is used to measure the velocity field which is then approximated using B-spline surfaces. The control points of the B-spline surfaces are directly used as the tracking variables in a grid-based Kalman filtering model. The limitations of the Gaussian distribution assumption in the Kalman filter are overcome by the large capture range of the elastic matching algorithm. The B-spline approximation of the velocity field is used to update the spectral features of the tracked objects in the grid-based Kalman filter model. The dynamic nature of these spectral features are subsequently used to reason about occlusion. Experimental results on tracking of multiple objects in real-time video are presented.

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“…In a Kalman particle filter, the Kalman filter generates Gaussian distributions for each particle from which one performs the sampling. It has been introduced into the visual tracking field and better performances are observed [10,11]. Nevertheless the price of this improvement is much extra computation.…”

Section: Introductionmentioning

confidence: 99%

Augmented particle filtering for efficient visual tracking

Shen

Brooks

Hengel

2005

IEEE International Conference on Image Processing 2005

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Visual tracking is one of the key tasks in computer vision. The particle filter algorithm has been extensively used to tackle this problem due to its flexibility. However the conventional particle filter uses system transition as the proposal distribution, frequently resulting in poor priors for the filtering step. The main reason is that it is difficult, if not impossible, to accurately model the target's motion. Such a proposal distribution does not take into account the current observations. It is not a trivial task to devise a satisfactory proposal distribution for the particle filter. In this paper we advance a general augmented particle filtering framework for designing the optimal proposal distribution. The essential idea is to augment a second filter's estimate into the proposal distribution design. We then show that several existing improved particle filters can be rationalised within this general framework. Based on this framework we further propose variant algorithms for robust and efficient visual tracking. Experiments indicate that the augmented particle filters are more efficient and robust than the conventional particle filter.

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Visual contour tracking based on particle filters

Cited by 125 publications

References 18 publications

Accurate appearance-based Bayesian tracking for maneuvering targets

Accurate appearance-based Bayesian tracking for maneuvering targets

A Novel Tracking Framework Using Kalman Filtering and Elastic Matching

Augmented particle filtering for efficient visual tracking

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