We present an approach that incorporates appearance-adaptive models in a particle filter to
realize robust visual tracking and recognition algorithms. Tracking needs modeling …

In many real world applications, tracking must be performed reliably in real-time for
sufficiently long periods where target appearance and motion may sensibly change from …

In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical
target model. The features are combined in a single particle filter by weighting their …

Robust visual tracking has become an important topic in the field of computer vision. The
integration of cues such as color, edge strength and motion has proved to be a promising …

One challenge when tracking objects is to adapt the object representation depending on the
scene context to account for changes in illumination, coloring, scaling, etc. Here, we present …

Visual tracking usually involves an optimization process for estimating the motion of an
object from measured images in a video sequence. In this paper, a new evolutionary …

The paper proposes a method to keep the tracker robust to background clutters by online
selecting discriminative features from a large feature space. Furthermore, the feature …

A new particle filter-the Kernel Particle Filter (KPF)-is proposed for visual tracking in image
sequences. The KPF invokes kernels to form a continuous estimate of the posterior density …

This paper presents visual cues for object tracking in video sequences using particle
filtering. A consistent histogram-based framework is developed for the analysis of colour …

We describe a particle filtering method for vision based tracking of a hand held calibrated
camera in real-time. The ability of the particle filter to deal with non-linearities and non …