Kernel based robust object tracking using model updates and Gaussian pyramids

  title={Kernel based robust object tracking using model updates and Gaussian pyramids},
  author={Zahraa M. Ali and Sajid Hussain and Imtiaz Ahmad Taj},
  journal={Proceedings of the IEEE Symposium on Emerging Technologies, 2005.},
  • Z. AliS. HussainI. Taj
  • Published 19 December 2005
  • Computer Science
  • Proceedings of the IEEE Symposium on Emerging Technologies, 2005.
Visionbasedtracking, being a challenging engineering problem isone ofthehotresearch areas in*nachine vision. Inrecentstuidies Kernel based tracking usinig Bhattacharya similaritv mea.sutre is showntobean eficient techniquie fornon-rigid object tracking through thesequienceofimnages. In this paper we presented a robutst and efficient tracking approach Jbrtargets having larger motions ascompared totheir sizes. Outr tracking approach is based on calculating theGaussian pyramids ofthe… 

Figures from this paper

Applying centroid based adjustment to kernel based object tracking for improving localization

It is proposed that integrating the edge based target information as post processing step for updating estimated mean shift centroid in KBOT improves the localization problem and has achieve more precise tracking results as compared to original kernel based object tracking.

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

The proposed adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system and constructs the adaptive kernel shape in the high-dimensional shape space by performing nonlinear manifold learning technique.

A novel fast moving object contour tracking algorithm

A new Particle Filter Mean Shift Snake (PFMSS) algorithm is proposed which combines particle filter with mean shift snake to fulfill the estimation of the fast moving object contour.

Review on Kernel based Target Tracking for Autonomous Driving

The theoretical and experimental analysis allow us to conclude that the kernel based online subspace learning algorithm achieves a good trade-off between the stability and real-time processing for target tracking in the practical application environments of autonomous driving.

Gaussian Pyramid Based Multi-Scale GVF Snake for Mass Segmentation in Digitized Mammograms

This paper proposes one novel scheme for segmentation of breast mass in digitized mammograms, which is based on gradient vector flow (GVF) snake and multi-scale analysis using Gaussian pyramid and it reduces the number of unnecessary iterations.

Adaptive hydrological flow field modeling based on water body extraction and surface information

Abstract. Hydrological flow characteristic is one of the prime indicators for assessing flood. It plays a major part in determining drainage capability of the affected basin and also in the

Study on the Optimal Image Resolution for Image Segmentation

The experimental results illustrate that degree of image segmentation is directly related with the result of segmentation, and the degree ofimage segmentation presented in this paper is a good index to describe how well an image can be segmented in the viewpoint of quantitative and qualitative assessing.



Mean-shift blob tracking through scale space

  • R. Collins
  • Computer Science
    2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
  • 2003
Lindeberg's theory of feature scale selection based on local maxima of differential scale-space filters to the problem of selecting kernel scale for mean-shift blob tracking shows that a difference of Gaussian (DOG) mean- shift kernel enables efficient tracking of blobs through scale space.

Computer Vision Face Tracking For Use in a Perceptual User Interface

The mean shift algorithm is modified to deal with dynamically changing color probability distributions derived from video frame sequences, called the Continuously Adaptive Mean Shift (CAMSHIFT), which is used as a computer interface for controlling commercial computer games and for exploring immersive 3D graphic worlds.

The variable bandwidth mean shift and data-driven scale selection

The sample point estimator is defined, prove its convergence, and show its superiority over the fixed bandwidth procedure, and an alternative approach for data-driven scale selection which imposes a local structure on the data is studied.

Mean Shift, Mode Seeking, and Clustering

  • Yizong Cheng
  • Computer Science
    IEEE Trans. Pattern Anal. Mach. Intell.
  • 1995
Mean shift, a simple interactive procedure that shifts each data point to the average of data points in its neighborhood is generalized and analyzed and makes some k-means like clustering algorithms its special cases.

The estimation of the gradient of a density function, with applications in pattern recognition

Applications of gradient estimation to pattern recognition are presented using clustering and intrinsic dimensionality problems, with the ultimate goal of providing further understanding of these problems in terms of density gradients.

Kernel-Based Object Tracking

A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.

Mean shift and optimal prediction for efficient object tracking

A new paradigm for the efficient color-based tracking of objects seen from a moving camera is presented. The proposed technique employs the mean shift analysis to derive the target candidate that is