This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimens...

In this paper we review a novel algorithm Supervised Learning which is AI based approach and giving the system to training using Haar training algorithm and Clustering algorithm for tracking multiple vehicle objects in dynamic environments wherein under illumination conditions and the surrounding infrastructure is known. The proposed technique relies on trained and tested data provided by a haa...

Object tracking in real time video is a challenging task and has many necessary applications. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation application. In this research, we tried to develop a color-based particle filter. And the color distributions of video frames are integrated into particle filtering. Color distributions are applied because of ...

Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model. Abrupt motion is not subject to motion continuity and smoothness. To assuage this, we deem tracking as an optimisation problem and propose a novel abrupt motion tracker that based on swarm intelligence the SwaTrack. Unlike existing swarm-based fil...

[1] Particle tracking algorithms are very useful methods to model conservative transport in surface and subsurface hydrological systems. Recently, a novel ad hoc particle-based method was proposed to account for multicomponent reactive transport by Benson and Meerschaert (2008). This one-dimensional particle method has been shown to match theoretical predictions, but, to date, there has been no...

This study aims at developing a particle method to detect faint debris, which is hardly seen in single image, from an image sequence based on a track-before-detect (TBD) method. TBD methods try to track targets without explicitly detecting the signals followed by evaluation of goodness of each track and obtaining detection results. This study proposes a particle based TBD (P-TBD) method consist...

Automatic lane tracking involves estimating the underlying signal from a sequence of noisy signal observations. Many models and methods have been proposed for lane tracking, and dynamic targets tracking in general. The Kalman Filter is a widely used method that works well on linear Gaussian models. But this paper shows that Kalman Filter is not suitable for lane tracking, because its Gaussian o...

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...

This paper proposes a visual object contour tracking algorithm using a multi-cue fusion particle filter. A novel contour evolution energy is proposed which integrates an incrementally learnt model of object appearance with a parametric snake model. This energy function is combined with a mixed cascade particle filter tracking algorithm which fuses multiple observation models for object contour ...

In the process of object tracking, the major problem is how to mark the tracking box of the object. Moreover, multi-objects tracking is also difficult. This paper proposed and efficient fast object-tracking scheme based on motion-vector-located pattern match, which adopts motion vector of Mpeg2 to mark the moving targets in static video in order to mark and locate the targets automatically and ...