To make the kernel-based tracking algorithms more reliable, in this work, we mainly deal with two major singular cases in kernel-based tracking, concerned with kernel observability and tracking stability. Singular kernel observability indicates that the motions of interest cannot be uniquely recovered by the kernel. We present a novel multiple collaborative kernel approach, in which a complex m...

In this work, a new method is proposed for hand tracking based on a density approximation and optimization method. Considering tracking as a classification problem, we train an approxiator to recognize hands from its background. This procedure is done by extracting feature vector of every pixel in the first frame and then building an approximator to construct a virtual optimized surface of pixe...

We present a solution for realtime tracking of a planar pattern. Tracking is seen as the estimation of a parametric function between observations and motion and we propose an extension of the learning based approach presented simultaneously by Cootes and al. and by Jurie and Dhome. We show that the hyperplane classic algorithm is a specific case of a more generic linearly-weighted sum of fixed ...

Object tracking finds its application in several computer vision applications, such as video compression, surveillance, robotics etc. Moving object detection and tracking are important steps in object recognition, context analysis and indexing processes for visual surveillance systems. It is a big challenge for researchers to make a decision on which tracking algorithm is more suitable for whic...

In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new ...

The tracking of motion of color object and detection of objects in real time video sequences is the important task in computer vision, video processing, image processing, intelligent system, etc. In this paper, we proposed Continuously Adaptive Mean Shift (CAMSHIFT) tracking with Color, Scale and Direction. In the CAMSHIFT the scale of the kernel is automatically altered in accordance with the ...

An object tracking algorithm that uses a novel simple symmetric similarity function between spatially-smoothed kernel-density estimates of the model and target distributions is proposed and tested. The similarity measure is based on the expectation of the density estimates over the model or target images. The density is estimated using radial-basis kernel functions which measure the affinity be...

This paper describes contributions to two problems related to visual tracking: control model design and observation process design. We describe the use of kernel-based Bayesian filtering for the tracking control procedure, and feature-based tracking to improve the observation process of tracking. In the kernelbased Bayesian filtering framework, the analytical representation of density functions...

Single or multi target tracking is one of the extensively studied areas in the field of computer vision. Object detection between successive frames a key step in tracking algorithm. This paper proposes a method which uses image correlation between two images for target detection in the consecutive frames. This Image tracking method focuses on a frequency domain based approach using two dimensio...