We describe a novel method whereby a particle filter is used to create a potential field for robot control without prior clustering. We show an application of this technique to control a team of mobile robots to cooperatively locate and track a moving target. The particle filter models a probability distribution over the estimated location of the target, providing robust tracking despite freque...

automatically updated during the matching process, This paper describes a real-time tracking system which detects an object entering into the field of view of camera and executes the tracking of the detected object by controlling a servo device so that a target object always lies at the center of an image frame. In order to detect and track a moving object, we basically apply a model matching s...

Tracking underwater targets is a focused application area in modern underwater defence systems. Using traditional techniques based on imaging or sensor arrays may be difficult and impractical in some mission-critical systems. Alternatively, the underwater wireless sensor network UWSN is able to offer a promising solution. This paper tackles the problem of accurately tracking underwater targets ...

Ronald Mahler’s Probability Hypothesis Density (PHD) provides a promising framework for the passive coherent location of targets observed via multiple bistatic radar measurements. We apply a particle filter implementation of the Bayesian PHD filter to target tracking using both range and Doppler measurements from a simple non-directional receiver that exploits non-coöperative FM radio transmitt...

Correlation filters have recently attracted attention in visual tracking due to their efficiency and high performance. However, their application to long-term tracking is somewhat limited since these trackers are not equipped with mechanisms to cope with challenging cases like partial occlusion, deformation or scale changes. In this paper, we propose a deformable part-based correlation filter t...

The (Extended) Kalman filter has been established as a standard method for object tracking. While a constraining motion model stabilizes the tracking results given noisy measurements, it limits the ability to follow an object in non-modeled maneuvers. In the context of a stereo-vision based vehicle tracking approach, we propose and compare three different strategies to automatically adapt the d...

In this paper, a target tracking algorithm is proposed by combining the improved Mean Shift algorithm with the adaptive Kalman filter. For a selected moving object, frame difference and region growing methods are used to segment target and extract the dominant color. In the tracking process, the initial iterative position is obtained by adaptive Kalman filter in each frame. The tracking result ...

This paper proposes a novel hybrid active power filter (HAPF) topology based on the cascaded connection of the AC-side capacitor and the third-order LCL-filter, which has the advantage of the conventional hybrid filter and the LCL-filter in terms of reduced dclink voltage and better switching ripple attenuation. The robust deadbeat control law is derived for the current loop, with special empha...

Hand gesture tracking is one of core method in the field of Human Computer Interaction. In this paper we proposed a tracking method based on Particle Filter framework. Particle Filter algorithm provides a powerful open framework for target tracking and can efficiently deal with motion estimation under the non-linear and nonGaussian environments. The novelty of our method is in the combination o...