In multi-target visual tracking, tracking failure due to missassociation can often arise from the presence of occlusions between targets. To cope with this problem, we propose the predictive estimation method that iterates occlusion prediction and occlusion status update using occlusion activity detection by utilizing joint probabilistic data association filter in order to track each target bef...

Particle filtering is a technique used for filtering non-linear and non-Gaussian dynamical systems. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. This paper presents an improved algorithm to get better convergence. This algorithm uses...

The application of kalman filter in tracking the maneuver target is not available as it is used in tracking the target of uniform motion. Therefore, a improved method for tracking a maneuver target is proposed. In the proposed approach, the maneuver detector provides the estimate of time instant at which a target starts to maneuver, when a target maneuver is determined, the kalman filter model ...

This paper presents a model-based approach to monocular tracking of human body using a non-calibrated camera. The tracking in monocular images is realized using a particle filter and an articulated 3D model with a cylinder-based representation of the body. In modeling the visual appearance of the person we employ appearance-adaptive models. The predominant orientation of the gradient combined w...

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filte...

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but...

When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a model-data association problem. Another approach to solve the problem with computational complexity is to track only the first...

The main focus of this paper is to investigate how the co-operative nature of an ad-hoc sensor network can be exploited in order to reduce the complexity of accurate node locationing algorithms in sensor networks. We propose a new approach to target tracking called node-aided tracking that, unlike radar-like methods, exploits the two-way communication link of an ad-hoc sensor network. Further, ...

In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation ...

In this paper, the authors use Spatial Interaction Filters (SIF) to simulate human experts’ visual process in tracking spatial interactive objects. The algorithm includes spatial density based pixel clustering and object interaction descriptions, such as Contact Area Index (CAI) and correlation filter. The algorithm is designed to automatically track the Harmful Algae Bloom (HAB) targets. In th...