Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filterbased approach, which is flexible to detect all kinds of lanes. A modified version of an Euclidean distance transform is applied to an edge map of a road image from a birds-eye view to provide information for boundary point detection. An effici...

The recently proposed use of the structural similarity measure, in the particle filter-based video tracker has been shown to improve the tracking performance, compared to similar methods using the colour or edge histograms and Bhattacharyya distance. However, the combined use of the structural similarity and a particle filter results in a computationally complex tracker that may not be suitable...

In the visual tracking domain, Particle Filtering (PF) can become quite inefficient when being applied into high dimensional state space. Rao-Blackwellisation [1] has been shown to be an effective method to reduce the size of the state space by marginalizing out some of the variables analytically . In this paper based on our previous work [3] we propose an RBPF tracking algorithm with adaptive ...

An algorithm is developed to significantly reduce the on-node footprint of cross section memory in Monte Carlo particle tracking algorithms. The classic method of per-node replication of cross section data is replaced by a memory server model, in which the read-only lookup tables reside on a remote set of disjoint processors. The main particle tracking algorithm is then modified in such a way a...

In this article, we demonstrate a hybrid tracking method for scaled and orientated objects in video. Histogram-based mean shift can track objects quickly. However, it does not utilize shape or dynamics information and typically employs a convex bounding box, such as a rectangle or an ellipse that is decoupled from object scale and orientation. Active shape models and particle filters cope with ...

Tracking articulated hand motion from visual observations is challenging mainly due to the high dimensionality of the state space. Dense sampling is difficult to be performed in such high-dimensional space, so the traditional particle filtering can’t track articulated motion well. In this paper, we propose a new algorithm by combining differential evolution with a particle filter, to track the ...

Compared with affine transformation, projection transformation represents the process of imaging objects more accurately. This paper proposes a novel object tracking method using particle filtering with dual manifold models. One is the covariance manifold used for the object observation model, and the other is the geometric deformation on SL(3) group, where the rank of projection transformation...

In this paper, we propose the object tracking method based on color histograms and particle filtering. Particle filtering is a time series filter for estimating a state using probabilistic approach. Unlike deterministic approach such as template matching algorithm, it is more robust to occlusion or clutter because of its having many hypotheses. Moreover, color histograms are robust to partial o...

Light-based imaging and microscopy techniques are compelling diagnostic tools that provide straightforward data. Furthermore, single-particle targeting and tracking has the potential to provide new insight on myriad phenomenon, such as small particle diffusion, particle-membrane interaction, singleparticle microfluidics, and many aspects of microbiology and physiology. However, limitations exis...